Building equipment with predictive control

ABSTRACT

A central energy facility (CEF) includes a plurality of powered CEF components, a battery unit, and a predictive CEF controller. The powered CEF components include a chiller unit and a cooling tower. The battery unit is configured to store electric energy from an energy grid and discharge the stored electric energy for use in powering the powered CEF components. The predictive CEF controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from the energy grid and an optimal amount of electric energy to store in the battery unit or discharge from the battery unit for use in powering the powered CEF components at each time step of an optimization period.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 62/524,325 filed Jun. 23, 2017, the entiredisclosure of which is incorporated by reference herein.

BACKGROUND

The present disclosure relates generally to building equipment withpredictive control and more particularly to HVAC equipment such aschillers, boilers, cooling towers, valves, pumps, and other types ofequipment for use in a central energy facility or building HVAC system.

A central energy facility (CEF) includes various types of HVAC equipmentconfigured to provide heating or cooling for a building. For example, aCEF may include chillers, boilers, heat recovery chillers, coolingtowers, valves, pumps, actuators, and other type of equipment configuredto heat or cool a working fluid circulated to a building. The heated orcooled fluid can be provided to an air handling unit or rooftop unit inorder to exchange heat with an airflow provided to one or more zones ofthe building.

The equipment of a CEF may include several components that consume powerduring operation. For example, a chiller may include a compressorconfigured to circulate a refrigerant through a refrigeration circuit. Acooling tower may include one or more fans configured to facilitateairflow through the cooling tower. Valve, actuators, and pumps may alsoconsume power during operation. It would be desirable to minimize thepower consumption of these and other power-consuming components in orderto reduce the cost of energy consumed by the CEF.

SUMMARY

One implementation of the present disclosure is a central energyfacility (CEF). The CEF includes a plurality of powered CEF components,a battery unit, and a predictive CEF controller. The powered CEFcomponents include a chiller unit and a cooling tower. The battery unitis configured to store electric energy from an energy grid and dischargethe stored electric energy for use in powering the powered CEFcomponents. The predictive CEF controller is configured to optimize apredictive cost function to determine an optimal amount of electricenergy to purchase from the energy grid and an optimal amount ofelectric energy to store in the battery unit or discharge from thebattery unit for use in powering the powered CEF components at each timestep of an optimization period.

In some embodiments, the CEF includes one or more photovoltaic panelsconfigured to collect photovoltaic energy. The predictive CEF controllermay be configured to determine an optimal amount of the photovoltaicenergy to store in the battery unit and an optimal amount of thephotovoltaic energy to be consumed by the powered CEF components at eachtime step of the optimization period.

In some embodiments, the predictive cost function accounts for a cost ofthe electric energy purchased from the energy grid at each time step ofthe optimization period and a cost savings resulting from dischargingstored electric energy from the battery unit at each time step of theoptimization period.

In some embodiments, the predictive CEF controller is configured toreceive energy pricing data defining a cost per unit of electric energypurchased from the energy grid at each time step of the optimizationperiod and use the energy pricing data as inputs to the predictive costfunction.

In some embodiments, the predictive cost function accounts for a demandcharge based on a maximum power consumption of the CEF during a demandcharge period that overlaps at least partially with the optimizationperiod. The predictive CEF controller may be configured to receiveenergy pricing data defining the demand charge and to use the energypricing data as inputs to the predictive cost function.

In some embodiments, the predictive CEF controller includes an economiccontroller configured to determine optimal power setpoints for thepowered CEF components and for the battery unit at each time step of theoptimization period, a tracking controller configured to use the optimalpower setpoints to determine optimal temperature setpoints at each timestep of the optimization period, and an equipment controller configuredto use the optimal temperature setpoints to generate control signals forthe powered CEF components and for the battery unit at each time step ofthe optimization period.

Another implementation of the present disclosure is an air-cooledchiller unit. The air-cooled chiller unit includes a refrigerationcircuit, a plurality of powered chiller components, a battery unit, anda predictive chiller controller. The refrigeration circuit includes anevaporator and a condenser. The powered chiller components include acompressor configured to circulate a refrigerant through therefrigeration circuit and a fan configured to provide cooling for thecondenser. The battery unit is configured to store electric energy froman energy grid and discharge the stored electric energy for use inpowering the powered chiller components. The predictive chillercontroller is configured to optimize a predictive cost function todetermine an optimal amount of electric energy to purchase from theenergy grid and an optimal amount of electric energy to store in thebattery unit or discharge from the battery unit for use in powering thepowered chiller components at each time step of an optimization period

In some embodiments, the air-cooled chiller unit includes one or morephotovoltaic panels configured to collect photovoltaic energy. Thepredictive chiller controller may be configured to determine an optimalamount of the photovoltaic energy to store in the battery unit and anoptimal amount of the photovoltaic energy to be consumed by the poweredchiller components at each time step of the optimization period.

In some embodiments, the predictive cost function accounts for a cost ofthe electric energy purchased from the energy grid at each time step ofthe optimization period and a cost savings resulting from dischargingstored electric energy from the battery unit at each time step of theoptimization period.

In some embodiments, the predictive chiller controller is configured toreceive energy pricing data defining a cost per unit of electric energypurchased from the energy grid at each time step of the optimizationperiod and use the energy pricing data as inputs to the predictive costfunction.

In some embodiments, the predictive cost function accounts for a demandcharge based on a maximum power consumption of the air-cooled chillerunit during a demand charge period that overlaps at least partially withthe optimization period. The predictive chiller controller may beconfigured to receive energy pricing data defining the demand charge andto use the energy pricing data as inputs to the predictive costfunction.

In some embodiments, the predictive chiller controller includes aneconomic controller configured to determine optimal power setpoints forthe powered chiller components and for the battery unit at each timestep of the optimization period, a tracking controller configured to usethe optimal power setpoints to determine optimal temperature setpointsat each time step of the optimization period, and an equipmentcontroller configured to use the optimal temperature setpoints togenerate control signals for the powered chiller components and for thebattery unit at each time step of the optimization period.

Another implementation of the present disclosure is a pump unit. Thepump unit includes a pump, a battery unit, and a predictive pumpcontroller. The pump is configured to circulate a fluid through a fluidcircuit. The battery unit is configured to store electric energy from anenergy grid and discharge the stored electric energy for use in poweringthe pump. The predictive pump controller is configured to optimize apredictive cost function to determine an optimal amount of electricenergy to purchase from the energy grid and an optimal amount ofelectric energy to store in the battery unit or discharge from thebattery unit for use in powering the pump at each time step of anoptimization period.

In some embodiments, the predictive cost function accounts for a cost ofthe electric energy purchased from the energy grid at each time step ofthe optimization period and a cost savings resulting from dischargingstored electric energy from the battery unit at each time step of theoptimization period.

In some embodiments, the predictive pump controller is configured toreceive energy pricing data defining a cost per unit of electric energypurchased from the energy grid at each time step of the optimizationperiod and use the energy pricing data as inputs to the predictive costfunction.

In some embodiments, the predictive cost function accounts for a demandcharge based on a maximum power consumption of the pump unit during ademand charge period that overlaps at least partially with theoptimization period. The predictive pump controller may be configured toreceive energy pricing data defining the demand charge and to use theenergy pricing data as inputs to the predictive cost function.

In some embodiments, the predictive pump controller includes an economiccontroller configured to determine optimal power setpoints for the pumpand for the battery unit at each time step of the optimization period, atracking controller configured to use the optimal power setpoints todetermine optimal flow setpoints or pressure setpoints at each time stepof the optimization period, and an equipment controller configured touse the optimal flow setpoints or pressure setpoints to generate controlsignals for the pump and for the battery unit at each time step of theoptimization period.

Another implementation of the present disclosure is a cooling towerunit. The cooling tower unit includes one or more powered cooling towercomponents, a battery unit, and a predictive cooling tower controller.The cooling tower components include at least one of a fan and a pump.The battery unit is configured to store electric energy from an energygrid and discharge the stored electric energy for use in powering thepowered cooling tower components. The predictive cooling towercontroller is configured to optimize a predictive cost function todetermine an optimal amount of electric energy to purchase from theenergy grid and an optimal amount of electric energy to store in thebattery unit or discharge from the battery unit for use in powering thepowered cooling tower components at each time step of an optimizationperiod.

In some embodiments, the cooling tower unit includes one or morephotovoltaic panels configured to collect photovoltaic energy. Thepredictive cooling tower controller may be configured to determine anoptimal amount of the photovoltaic energy to store in the battery unitand an optimal amount of the photovoltaic energy to be consumed by thepowered cooling tower components at each time step of the optimizationperiod.

In some embodiments, the predictive cost function accounts for a cost ofthe electric energy purchased from the energy grid at each time step ofthe optimization period and a cost savings resulting from dischargingstored electric energy from the battery unit at each time step of theoptimization period.

In some embodiments, the predictive cooling tower controller isconfigured to receive energy pricing data defining a cost per unit ofelectric energy purchased from the energy grid at each time step of theoptimization period and use the energy pricing data as inputs to thepredictive cost function.

In some embodiments, the predictive cost function accounts for a demandcharge based on a maximum power consumption of the cooling tower unitduring a demand charge period that overlaps at least partially with theoptimization period. The predictive cooling tower controller may beconfigured to receive energy pricing data defining the demand charge andto use the energy pricing data as inputs to the predictive costfunction.

In some embodiments, the predictive cooling tower controller includes aneconomic controller configured to determine optimal power setpoints forthe powered cooling tower components and for the battery unit at eachtime step of the optimization period, a tracking controller configuredto use the optimal power setpoints to determine optimal temperaturesetpoints at each time step of the optimization period, and an equipmentcontroller configured to use the optimal temperature setpoints togenerate control signals for the powered cooling tower components andfor the battery unit at each time step of the optimization period.

Another implementation of the present disclosure is a valve unit. Thevalve unit includes a valve, one or more powered valve components, abattery unit, and a predictive valve controller. The valve is configuredto control a flowrate of a fluid through a fluid conduit. The poweredvalve components include a valve actuator coupled to the valve andconfigured to modulate a position of the valve. The battery unit isconfigured to store electric energy from an energy grid and dischargethe stored electric energy for use in powering the powered valvecomponents. The predictive valve controller is configured to optimize apredictive cost function to determine an optimal amount of electricenergy to purchase from the energy grid and an optimal amount ofelectric energy to store in the battery unit or discharge from thebattery unit for use in powering the powered valve components at eachtime step of an optimization period.

In some embodiments, the predictive cost function accounts for a cost ofthe electric energy purchased from the energy grid at each time step ofthe optimization period and a cost savings resulting from dischargingstored electric energy from the battery unit at each time step of theoptimization period.

In some embodiments, the predictive valve controller is configured toreceive energy pricing data defining a cost per unit of electric energypurchased from the energy grid at each time step of the optimizationperiod and use the energy pricing data as inputs to the predictive costfunction.

In some embodiments, the predictive cost function accounts for a demandcharge based on a maximum power consumption of the valve unit during ademand charge period that overlaps at least partially with theoptimization period. The predictive valve controller may be configuredto receive energy pricing data defining the demand charge and to use theenergy pricing data as inputs to the predictive cost function.

In some embodiments, the predictive valve controller includes aneconomic controller configured to determine optimal power setpoints forthe powered valve components and for the battery unit at each time stepof the optimization period, a tracking controller configured to use theoptimal power setpoints to determine optimal position setpoints at eachtime step of the optimization period, and an equipment controllerconfigured to use the optimal temperature setpoints to generate controlsignals for the powered valve components and for the battery unit ateach time step of the optimization period.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a drawing of a building equipped with a HVAC system, accordingto some embodiments.

FIG. 2 is a drawing of a central energy facility (CEF) which can be usedto provide heating or cooling to the building of FIG. 1, according tosome embodiments.

FIG. 3 is a drawing of a CEF with a battery unit and a predictive CEFcontroller, according to some embodiments.

FIG. 4 is a block diagram of a predictive CEF control system includingthe battery unit and predictive CEF controller of FIG. 3, according tosome embodiments.

FIG. 5 is a block diagram illustrating the predictive CEF controller ofFIG. 3 in greater detail, according to some embodiments.

FIG. 6 is a graph of a user interface which can be generated by thepredictive CEF controller of FIG. 3, according to some embodiments.

FIG. 7 is a drawing of an air-cooled chiller unit with a battery unitand a predictive chiller controller, according to some embodiments.

FIG. 8 is a block diagram of the air-cooled chiller unit of FIG. 7,according to some embodiments.

FIG. 9 is a block diagram of a predictive chiller control systemincluding the battery unit and predictive chiller controller of FIG. 7,according to some embodiments.

FIG. 10 is a block diagram illustrating the predictive chillercontroller of FIG. 7 in greater detail, according to some embodiments.

FIG. 11 is a drawing of a pump unit with a battery unit and a predictivepump controller, according to some embodiments.

FIG. 12 is a block diagram of the pump unit of FIG. 11, according tosome embodiments.

FIG. 13 is a block diagram of a predictive pump control system includingthe battery unit and predictive pump controller of FIG. 11, according tosome embodiments.

FIG. 14 is a block diagram illustrating the predictive pump controllerof FIG. 11 in greater detail, according to some embodiments.

FIG. 15 is a drawing of a cooling tower unit with a battery unit and apredictive cooling tower controller, according to some embodiments.

FIG. 16 is a block diagram of a predictive cooling tower control systemincluding the battery unit and predictive cooling tower controller ofFIG. 15, according to some embodiments.

FIG. 17 is a block diagram illustrating the predictive cooling towercontroller of FIG. 15 in greater detail, according to some embodiments.

FIG. 18 is a drawing of a valve unit with a battery unit and apredictive valve controller, according to some embodiments.

FIG. 19 is a block diagram of the valve unit of FIG. 18, according tosome embodiments.

FIG. 20 is a block diagram of a predictive valve control systemincluding the battery unit and predictive valve controller of FIG. 18,according to some embodiments.

FIG. 21 is a block diagram illustrating the predictive valve controllerof FIG. 18 in greater detail, according to some embodiments.

DETAILED DESCRIPTION Building and HVAC System

Referring now to FIG. 1, a perspective view of a building 10 is shown.Building 10 is served by a BMS. A BMS is, in general, a system ofdevices configured to control, monitor, and manage equipment in oraround a building or building area. A BMS can include, for example, aHVAC system, a security system, a lighting system, a fire alertingsystem, any other system that is capable of managing building functionsor devices, or any combination thereof.

The BMS that serves building 10 includes a HVAC system 100. HVAC system100 can include a plurality of HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 mayprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 may use the heated or chilled fluid toheat or cool an airflow provided to building 10.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 may use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 can be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104can be transported to AHU 106 via piping 108.

AHU 106 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 may deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and mayprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 may receive input from sensorslocated within AHU 106 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Central Energy Facility

Referring now to FIG. 2, a block diagram of a central energy facility(CEF) 200 is shown, according to some embodiments. In variousembodiments, CEF 200 may supplement or replace waterside system 120 inHVAC system 100 or can be implemented separate from HVAC system 100.When implemented in HVAC system 100, CEF 200 can include a subset of theHVAC devices in HVAC system 100 (e.g., boiler 104, chiller 102, pumps,valves, etc.) and may operate to supply a heated or chilled fluid to AHU106. The HVAC devices of CEF 200 can be located within building 10(e.g., as components of waterside system 120) or at an offsite location.

CEF 200 is shown to include a plurality of subplants 202-212 including aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve thermal energy loads (e.g.,hot water, cold water, heating, cooling, etc.) of a building or campus.For example, heater subplant 202 can be configured to heat water in ahot water loop 214 that circulates the hot water between heater subplant202 and building 10. Chiller subplant 206 can be configured to chillwater in a cold water loop 216 that circulates the cold water betweenchiller subplant 206 building 10. Heat recovery chiller subplant 204 canbe configured to transfer heat from cold water loop 216 to hot waterloop 214 to provide additional heating for the hot water and additionalcooling for the cold water. Condenser water loop 218 may absorb heatfrom the cold water in chiller subplant 206 and reject the absorbed heatin cooling tower subplant 208 or transfer the absorbed heat to hot waterloop 214. Hot TES subplant 210 and cold TES subplant 212 may store hotand cold thermal energy, respectively, for subsequent use.

Hot water loop 214 and cold water loop 216 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO₂, etc.) can be used inplace of or in addition to water to serve thermal energy loads. In otherembodiments, subplants 202-212 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to CEF 200 are withinthe teachings of the present disclosure.

Each of subplants 202-212 can include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 may alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 may also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in CEF 200 (e.g., pumps222, 224, 228, 230, 234, 236, and/or 240) or pipelines in CEF 200include an isolation valve associated therewith. Isolation valves can beintegrated with the pumps or positioned upstream or downstream of thepumps to control the fluid flows in CEF 200. In various embodiments, CEF200 can include more, fewer, or different types of devices and/orsubplants based on the particular configuration of CEF 200 and the typesof loads served by CEF 200.

Central Energy Facility with Battery Unit and Predictive Control

Referring now to FIG. 3, a central energy facility (CEF) 300 with abattery unit 302 and predictive CEF controller 304 is shown, accordingto some embodiments. CEF 300 can be configured to provide cooling to acooling load 322. Cooling load 322 can include, for example, a buildingzone, a supply airstream flowing through an air duct, an airflow in anair handling unit or rooftop unit, fluid flowing through a heatexchanger, a refrigerator or freezer, a condenser or evaporator, acooling coil, or any other type of system, device, or space whichrequires cooling. In some embodiments, a pump 318 circulates a chilledfluid to cooling load 322 via a chilled fluid circuit 336. The chilledfluid can absorb heat from cooling load 322, thereby providing coolingto cooling load 322 and warming the chilled fluid.

CEF 300 is shown to include a cooling tower 312 and a chiller 320.Cooling tower 312 can be configured to cool the water in cooling towercircuit 332 by transferring heat from the water to outside air. In someembodiments, a pump 316 circulates water through cooling tower 312 viacooling tower circuit 332. Cooling tower 312 may include a fan 314 whichcauses cool air to flow through cooling tower 312. Cooling tower 312places the cool air in a heat exchange relationship with the warmerwater, thereby transferring heat from warmer water to the cooler air.Cooling tower 312 can provide cooling for a condenser 326 of chiller320. Condenser 326 can transfer heat from the refrigerant inrefrigeration circuit 334 to the water in cooling tower circuit 332.Although cooling tower circuit 332 is shown and described as circulatingwater, it should be understood that any type of coolant or working fluid(e.g., water, glycol, CO₂, etc.) can be used in cooling tower circuit332.

Chiller 320 is shown to include a condenser 326, a compressor 328, anevaporator 330, and an expansion device 324. Compressor 328 can beconfigured to circulate a refrigerant between condenser 326 andevaporator 330 via refrigeration circuit 334. Compressor 328 operates tocompress the refrigerant to a high pressure, high temperature state. Thecompressed refrigerant flows through condenser 326, which transfers heatfrom the refrigerant in refrigeration circuit 334 to the water incooling tower circuit 332. The cooled refrigerant then flows throughexpansion device 324, which expands the refrigerant to a lowtemperature, low pressure state. The expanded refrigerant flows throughevaporator 330, which transfers heat from the chilled fluid in chilledfluid circuit 336 to the refrigerant in refrigeration circuit 334.

In some embodiments, CEF 300 includes multiple chillers 320. Each ofchillers 320 can be arranged in parallel and configured to providecooling for the fluid in chilled fluid circuit 336. The set of chillers320 may have a cooling capacity of approximately 1-3 MW or 1000-6000tons in some embodiments. Similarly, CEF 300 can include multiplecooling towers 312. Each of the cooling towers 312 can be arranged inparallel and configured to provide cooling for the water in coolingtower circuit 332. Although only cooling components are shown in FIG. 3,it is contemplated that CEF 300 can include heating components in someembodiments. For example, CEF 300 may include one or more boilers, heatrecovery chillers, steam generators, or other devices configured toprovide heating. In some embodiments, CEF 300 includes some or all ofthe components of CEF 200, as described with reference to FIG. 2.

Still referring to FIG. 3, CEF 300 is shown to include a battery unit302. In some embodiments, battery unit 302 includes one or morephotovoltaic (PV) panels 308. PV panels 308 may include a collection ofphotovoltaic cells. The photovoltaic cells are configured to convertsolar energy (i.e., sunlight) into electricity using a photovoltaicmaterial such as monocrystalline silicon, polycrystalline silicon,amorphous silicon, cadmium telluride, copper indium galliumselenide/sulfide, or other materials that exhibit the photovoltaiceffect. In some embodiments, the photovoltaic cells are contained withinpackaged assemblies that form PV panels 308. Each PV panel 308 mayinclude a plurality of linked photovoltaic cells. PV panels 308 maycombine to form a photovoltaic array.

In some embodiments, PV panels 308 are configured to maximize solarenergy collection. For example, battery unit 302 may include a solartracker (e.g., a GPS tracker, a sunlight sensor, etc.) that adjusts theangle of PV panels 308 so that PV panels 308 are aimed directly at thesun throughout the day. The solar tracker may allow PV panels 308 toreceive direct sunlight for a greater portion of the day and mayincrease the total amount of power produced by PV panels 308. In someembodiments, battery unit 302 includes a collection of mirrors, lenses,or solar concentrators configured to direct and/or concentrate sunlighton PV panels 308. The energy generated by PV panels 308 may be stored inbattery cells 306 and/or used to power various components of CEF 300.

In some embodiments, battery unit 302 includes one or more battery cells306. Battery cells 306 are configured to store and discharge electricenergy (i.e., electricity). In some embodiments, battery unit 302 ischarged using electricity from an external energy grid (e.g., providedby an electric utility). The electricity stored in battery unit 302 canbe discharged to power one or more powered components of CEF 300 (e.g.,cooling tower 312, fan 314, chiller 320, pumps 316-318, etc.).Advantageously, battery unit 302 allows CEF 300 to draw electricity fromthe energy grid and charge battery unit 302 when energy prices are lowand discharge the stored electricity when energy prices are high totime-shift the electric load of CEF 300. In some embodiments, batteryunit 302 has sufficient energy capacity (e.g., 6-12 MW-hours) to powerCEF 300 for approximately 4-6 hours when operating at maximum capacitysuch that battery unit 302 can be utilized during high energy costperiods and charged during low energy cost periods.

In some embodiments, predictive CEF controller 304 performs anoptimization process to determine whether to charge or discharge batteryunit 302 during each of a plurality of time steps that occur during anoptimization period. Predictive CEF controller 304 may use weather andpricing data 310 to predict the amount of heating/cooling required andthe cost of electricity during each of the plurality of time steps.Predictive CEF controller 304 can optimize an objective function thataccounts for the cost of electricity purchased from the energy grid overthe duration of the optimization period. In some embodiments, theobjective function also accounts for the cost of operating variouscomponents of CEF 300 (e.g., cost of natural gas used to fuel boilers).Predictive CEF controller 304 can determine an amount of electricity topurchase from the energy grid and an amount of electricity to store ordischarge from battery unit 302 during each time step. The objectivefunction and the optimization performed by predictive CEF controller 304are described in greater detail with reference to FIGS. 4-5.

Predictive CEF Control System

Referring now to FIG. 4, a block diagram of a predictive CEF controlsystem 400 is shown, according to some embodiments. Several of thecomponents shown in control system 400 may be part of CEF 300. Forexample, CEF 300 may include powered CEF components 402, battery unit302, predictive CEF controller 304, power inverter 410, and a powerjunction 412. Powered CEF components 402 may include any component ofCEF 300 that consumes power (e.g., electricity) during operation. Forexample, powered CEF components 402 are shown to include cooling towers404, chillers 406, and pumps 408. These components may be similar tocooling tower 312, chiller 320, and pumps 316-318, as described withreference to FIG. 3.

Power inverter 410 may be configured to convert electric power betweendirect current (DC) and alternating current (AC). For example, batteryunit 302 may be configured to store and output DC power, whereas energygrid 414 and powered CEF components 402 may be configured to consume andprovide AC power. Power inverter 410 may be used to convert DC powerfrom battery unit 302 into a sinusoidal AC output synchronized to thegrid frequency of energy grid 414 and/or powered CEF components 402.Power inverter 410 may also be used to convert AC power from energy grid414 into DC power that can be stored in battery unit 302. The poweroutput of battery unit 302 is shown as P_(bat). P_(bat) may be positiveif battery unit 302 is providing power to power inverter 410 (i.e.,battery unit 302 is discharging) or negative if battery unit 302 isreceiving power from power inverter 410 (i.e., battery unit 302 ischarging).

In some instances, power inverter 410 receives a DC power output frombattery unit 302 and converts the DC power output to an AC power outputthat can be provided to powered CEF components 402. Power inverter 410may synchronize the frequency of the AC power output with that of energygrid 414 (e.g., 50 Hz or 60 Hz) using a local oscillator and may limitthe voltage of the AC power output to no higher than the grid voltage.In some embodiments, power inverter 410 is a resonant inverter thatincludes or uses LC circuits to remove the harmonics from a simplesquare wave in order to achieve a sine wave matching the frequency ofenergy grid 414. In various embodiments, power inverter 410 may operateusing high-frequency transformers, low-frequency transformers, orwithout transformers. Low-frequency transformers may convert the DCoutput from battery unit 302 directly to the AC output provided topowered CEF components 402. High-frequency transformers may employ amulti-step process that involves converting the DC output tohigh-frequency AC, then back to DC, and then finally to the AC outputprovided to powered CEF components 402.

The power output of PV panels 308 is shown as P_(PV). The power outputP_(PV) of PV panels 308 can be stored in battery unit 302 and/or used topower powered CEF components 402. In some embodiments, PV panels 308measure the amount of power P_(PV) generated by PV panels 308 andprovides an indication of the PV power to predictive CEF controller 304.For example, PV panels 308 are shown providing an indication of the PVpower percentage (i.e., PV %) to predictive CEF controller 304. The PVpower percentage may represent a percentage of the maximum PV power atwhich PV panels 308 are currently operating.

Power junction 412 is the point at which powered CEF components 402,energy grid 414, PV panels 308, and power inverter 410 are electricallyconnected. The power supplied to power junction 412 from power inverter410 is shown as P_(bat). P_(bat) may be positive if power inverter 410is providing power to power junction 412 (i.e., battery unit 302 isdischarging) or negative if power inverter 410 is receiving power frompower junction 412 (i.e., battery unit 302 is charging). The powersupplied to power junction 412 from energy grid 414 is shown as P_(grid)and the power supplied to power junction 412 from PV panels 308 is shownas P_(PV). P_(bat), P_(PV), and P_(grid) combine at power junction 412to form P_(total) (i.e. P_(total)=P_(grid)+P_(bat)+P_(PV)______.P_(total) may be defined as the power provided to powered CEF components402 from power junction 412. In some instances, P_(total) is greaterthan P_(grid). For example, when battery unit 302 is discharging,P_(bat) may be positive which adds to the grid power P_(grid) and the PVpower P_(PV) when P_(bat) and P_(PV) combine with P_(grid) to formP_(total). In other instances, P_(total) may be less than P_(grid). Forexample, when battery unit 302 is charging, P_(bat) may be negativewhich subtracts from the grid power P_(grid) and the PV power P_(PV)when P_(bat), P_(PV), and P_(grid) combine to form P_(total).

Predictive CEF controller 304 can be configured to control powered CEFcomponents 402 and power inverter 410. In some embodiments, predictiveCEF controller 304 generates and provides a battery power setpointP_(sp,bat) to power inverter 410. The battery power setpoint P_(sp,bat)may include a positive or negative power value (e.g., kW) which causespower inverter 410 to charge battery unit 302 (when P_(sp,bat) isnegative) using power available at power junction 412 or dischargebattery unit 302 (when P_(sp,bat) is positive) to provide power to powerjunction 412 in order to achieve the battery power setpoint P_(sp,bat).

In some embodiments, predictive CEF controller 304 generates andprovides control signals to powered CEF components 402. Predictive CEFcontroller 304 may use a multi-stage optimization technique to generatethe control signals. For example, predictive CEF controller 304 mayinclude an economic controller configured to determine the optimalamount of power to be consumed by powered CEF components 402 at eachtime step during the optimization period. The optimal amount of power tobe consumed may minimize a cost function that accounts for the cost ofenergy consumed by CEF 300. The cost of energy may be based ontime-varying energy prices from electric utility 418. In someembodiments, predictive CEF controller 304 determines an optimal amountof power to purchase from energy grid 414 (i.e., a grid power setpointP_(sp,grid)) and an optimal amount of power to store or discharge frombattery unit 302 (i.e., a battery power setpoint P_(sp,bat)) at each ofthe plurality of time steps. Predictive CEF controller 304 may monitorthe actual power usage of powered CEF components 402 and may utilize theactual power usage as a feedback signal when generating the optimalpower setpoints.

Predictive CEF controller 304 may include a tracking controllerconfigured to generate temperature setpoints (e.g., a zone temperaturesetpoint T_(sp,zone), a chilled water temperature setpoint T_(sp,chw),etc.) that achieve the optimal amount of power consumption at each timestep. In some embodiments, predictive CEF controller 304 uses equipmentmodels for powered CEF components 402 to determine an amount of heatingor cooling that can be generated by CEF components 402 based on theoptimal amount of power consumption. Predictive CEF controller 304 canuse a zone temperature model in combination with weather forecasts froma weather service 416 to predict how the temperature of the buildingzone T_(zone) will change based on the power setpoints and/or thetemperature setpoints.

In some embodiments, predictive CEF controller 304 uses the temperaturesetpoints to generate the control signals for powered CEF components402. The control signals may include on/off commands, speed setpointsfor fans of cooling towers 404, power setpoints for compressors ofchillers 406, chilled water temperature setpoints for chillers 406,pressure setpoints or flow rate setpoints for pumps 408, or other typesof setpoints for individual devices of powered CEF components 402. Inother embodiments, the control signals may include the temperaturesetpoints (e.g., a zone temperature setpoint T_(sp,zone), a chilledwater temperature setpoint T_(sp,chw), etc.) generated by predictive CEFcontroller 304. The temperature setpoints can be provided to powered CEFcomponents 402 or local controllers for powered CEF components 402 whichoperate to achieve the temperature setpoints. For example, a localcontroller for chillers 406 may receive a measurement of the chilledwater temperature T_(chw) from chilled water temperature sensor and/or ameasurement the zone temperature T_(zone) from a zone temperaturesensor. The local controller can use a feedback control process (e.g.,PID, ESC, MPC, etc.) to increase or decrease the amount of coolingprovided by chillers 406 to drive the measured temperature(s) to thetemperature setpoint(s). Similar feedback control processes can be usedto control cooling towers 404 and/or pumps 408. The multi-stageoptimization performed by predictive CEF controller 304 is described ingreater detail with reference to FIG. 5.

Predictive CEF Controller

Referring now to FIG. 5, a block diagram illustrating predictive CEFcontroller 304 in greater detail is shown, according to an exemplaryembodiment. Predictive CEF controller 304 is shown to include acommunications interface 502 and a processing circuit 504.Communications interface 502 may facilitate communications betweencontroller 304 and external systems or devices. For example,communications interface 502 may receive measurements of the zonetemperature T_(zone) from zone temperature sensor 516 and measurementsof the power usage of powered CEF components 402. In some embodiments,communications interface 502 receives measurements of thestate-of-charge (SOC) of battery unit 302, which can be provided as apercentage of the maximum battery capacity (i.e., battery %).Communications interface 502 can receive weather forecasts from aweather service 416 and predicted energy costs and demand costs from anelectric utility 418. In some embodiments, predictive CEF controller 304uses communications interface 502 to provide control signals powered CEFcomponents 402 and power inverter 410.

Communications interface 502 may include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications external systems or devices. In various embodiments, thecommunications may be direct (e.g., local wired or wirelesscommunications) or via a communications network (e.g., a WAN, theInternet, a cellular network, etc.). For example, communicationsinterface 502 can include an Ethernet card and port for sending andreceiving data via an Ethernet-based communications link or network. Inanother example, communications interface 502 can include a Wi-Fitransceiver for communicating via a wireless communications network orcellular or mobile phone communications transceivers.

Processing circuit 504 is shown to include a processor 506 and memory508. Processor 506 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 506 isconfigured to execute computer code or instructions stored in memory 508or received from other computer readable media (e.g., CDROM, networkstorage, a remote server, etc.).

Memory 508 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 508 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory508 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 508 may be communicably connected toprocessor 506 via processing circuit 504 and may include computer codefor executing (e.g., by processor 506) one or more processes describedherein. When processor 506 executes instructions stored in memory 508for completing the various activities described herein, processor 506generally configures controller 304 (and more particularly processingcircuit 504) to complete such activities.

Still referring to FIG. 5, predictive CEF controller 304 is shown toinclude an economic controller 510, a tracking controller 512, and anequipment controller 514. Controllers 510-514 can be configured toperform a multi-state optimization process to generate control signalsfor power inverter 410 and powered CEF components 402. In briefoverview, economic controller 510 can optimize a predictive costfunction to determine an optimal amount of power to purchase from energygrid 414 (i.e., a grid power setpoint P_(sp,grid)), an optimal amount ofpower to store or discharge from battery unit 302 (i.e., a battery powersetpoint P_(sp,bat)), and/or an optimal amount of power to be consumedby powered CEF components 402 (i.e., a CEF power setpoint P_(sp,total))at each time step of an optimization period. Tracking controller 512 canuse the optimal power setpoints P_(sp,grid), P_(sp,bat), and/orP_(sp,total) to determine optimal temperature setpoints (e.g., a zonetemperature setpoint T_(sp,zone), a chilled water temperature setpointT_(sp,chw), etc.) and an optimal battery charge or discharge rate (i.e.,Bat_(C/D)). Equipment controller 514 can use the optimal temperaturesetpoints T_(sp,zone) or T_(sp,chw) to generate control signals forpowered CEF components 402 that drive the actual (e.g., measured)temperatures T_(zone) and/or T_(chw), to the setpoints (e.g., using afeedback control technique). Each of controllers 510-514 is described indetail below.

Economic Controller

Economic controller 510 can be configured to optimize a predictive costfunction to determine an optimal amount of power to purchase from energygrid 414 (i.e., a grid power setpoint P_(sp,grid)), an optimal amount ofpower to store or discharge from battery unit 302 (i.e., a battery powersetpoint P_(sp,bat)), and/or an optimal amount of power to be consumedby powered CEF components 402 (i.e., a CEF power setpoint P_(sp,total))at each time step of an optimization period. An example of a predictivecost function which can be optimized by economic controller 510 is shownin the following equation:

${\min (J)} = {{\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{chiller}(k)}\Delta \; t}} + {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{HRC}(k)}\Delta \; t}} + {\sum\limits_{k = 1}^{h}{{C_{gas}(k)}{F_{gas}(k)}\Delta \; t}} + {C_{DC}{\max\limits_{k}\left( {P_{grid}(k)} \right)}} - {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{bat}(k)}\Delta \; t}}}$

where C_(ec)(k) is the cost per unit of electricity (e.g., $/kWh)purchased from electric utility 418 during time step k, Pchiller(k) isthe power consumption (e.g., kW) of one or more chillers of CEF 300during time step k, P_(HRC)(k) is the power consumption of one or moreheat recovery chillers (HRCs) of CEF 300 at time step k, F_(gas)(k) isthe natural gas consumption of one or more boilers of CEF 300 at timestep k, C_(gas)(k) is the cost per unit of natural gas consumed by CEF300 at time step k, C_(DC) is the demand charge rate (e.g., $/kW), wherethe max( ) term selects the maximum electricity purchase of CEF 300(i.e., the maximum value of P_(grid)(k)) during any time step k of theoptimization period, P_(bat)(k) is the amount of power discharged frombattery unit 302 during time step k, and Δt is the duration of each timestep k. Economic controller 510 can optimize the predictive costfunction J over the duration of the optimization period (e.g., from timestep k=1 to time step k=h) to predict the total cost of operating CEF300 over the duration of the optimization period.

The first and second terms of the predictive cost function J representthe cost of electricity consumed by powered CEF components 402 over theduration of the optimization period. The values of the parameterC_(ec)(k) at each time step k can be defined by the energy costinformation provided by electric utility 418. In some embodiments, thecost of electricity varies as a function of time, which results indifferent values of C_(ec)(k) at different time steps k. The variablesP_(chiller)(k) and P_(HRC)(k) are decision variables which can beoptimized by economic controller 510. In some embodiments, the totalpower consumption P_(total)(k) of powered CEF components 402 at timestep k is equal to the sum of P_(chiller)(k) and P_(HRC)(k) (i.e.,P_(total)(k)=P_(chiller)(k)+P_(HRC)(k)). Accordingly, the first twoterms of the predictive cost function can be replaced with the summationΣ_(k=1) ^(h)C_(ec)(k)P_(total)(k)Δt in some embodiments.

The third term of the predictive cost function J represents the cost ofthe fuel (e.g., natural gas) consumed by CEF 300 over the duration ofthe optimization period. The values of C_(gas)(k) at each time step kcan be defined by the energy cost information provided by a natural gasutility. In some embodiments, the cost of gas varies as a function oftime, which results in different values of C_(gas)(k) at different timesteps k. The variable F_(gas)(k) is a decision variable which can beoptimized by economic controller 510.

The fourth term of the predictive cost function J represents the demandcharge. Demand charge is an additional charge imposed by some utilityproviders based on the maximum power consumption during an applicabledemand charge period. For example, the demand charge rate C_(DC) may bespecified in terms of dollars per unit of power (e.g., $/kW) and may bemultiplied by the peak power usage (e.g., kW) during a demand chargeperiod to calculate the demand charge. In the predictive cost functionJ, the demand charge rate C_(DC) may be defined by the demand costinformation received from electric utility 418. The variable P_(grid)(k)is a decision variable which can be optimized by economic controller 510in order to reduce the peak power usage max(P_(grid)(k)) that occursduring the demand charge period. Load shifting may allow economiccontroller 510 to smooth momentary spikes in the electric demand of CEF300 by storing energy in battery unit 302 when the power consumption ofpowered CEF components 402 is low. The stored energy can be dischargedfrom battery unit 302 when the power consumption of powered CEFcomponents 402 is high in order to reduce the peak power draw P_(grid)from energy grid 414, thereby decreasing the demand charge incurred.

The final term of the predictive cost function J represents the costsavings resulting from the use of battery unit 302. Unlike the previousterms in the cost function J, the final term subtracts from the totalcost. The values of the parameter C_(ec)(k) at each time step k can bedefined by the energy cost information provided by electric utility 418.In some embodiments, the cost of electricity varies as a function oftime, which results in different values of C_(ec)(k) at different timesteps k. The variable P_(bat)(k) is a decision variable which can beoptimized by economic controller 510. A positive value of P_(bat)(k)indicates that battery unit 302 is discharging, whereas a negative valueof P_(bat)(k) indicates that battery unit 302 is charging. The powerdischarged from battery unit 302 P_(bat)(k) can be used to satisfy someor all of the total power consumption P_(total)(k) of powered CEFcomponents 402, which reduces the amount of power P_(grid)(k) purchasedfrom energy grid 414 (i.e.,P_(grid)(k)=P_(total)(k)−P_(bat)(k)−P_(PV)(k)). However, chargingbattery unit 302 results in a negative value of P_(bat)(k) which adds tothe total amount of power P_(grid)(k) purchased from energy grid 414.

In some embodiments, the power P_(PV) provided by PV panels 308 is notincluded in the predictive cost function J because generating PV powerdoes not incur a cost. However, the power P_(PV) generated by PV panels308 can be used to satisfy some or all of the total power consumptionP_(total)(k) of powered CEF components 402, which reduces the amount ofpower P_(grid)(k) purchased from energy grid 414 (i.e.,P_(grid)(k)=P_(total)(k)−P_(bat)(k)−P_(PV)(k)). The amount of PV powerP_(PV) generated during any time step k can be predicted by economiccontroller 510. Several techniques for predicting the amount of PV powergenerated by PV panels are described in U.S. patent application Ser. No.15/247,869, U.S. patent application Ser. No. 15/247,844, and U.S. patentapplication Ser. No. 15/247,788. Each of these patent applications has afiling date of Aug. 25, 2016, and the entire disclosure of each of thesepatent applications is incorporated by reference herein.

Economic controller 510 can optimize the predictive cost function J overthe duration of the optimization period to determine optimal values ofthe decision variables at each time step during the optimization period.In some embodiments, the optimization period has a duration ofapproximately one day and each time step is approximately fifteenminutes. However, the durations of the optimization period and the timesteps can vary in other embodiments and can be adjusted by a user.Advantageously, economic controller 510 can use battery unit 302 toperform load shifting by drawing electricity from energy grid 414 whenenergy prices are low and/or when the power consumed by powered CEFcomponents 402 is low. The electricity can be stored in battery unit 302and discharged later when energy prices are high and/or the powerconsumption of powered CEF components 402 is high. This enables economiccontroller 510 to reduce the cost of electricity consumed by CEF 300 andcan smooth momentary spikes in the electric demand of CEF 300, therebyreducing the demand charge incurred.

Economic controller 510 can be configured to impose constraints on theoptimization of the predictive cost function J. In some embodiments, theconstraints include constraints on the temperature T_(zone) of abuilding zone served by CEF 300. Economic controller 510 can beconfigured to maintain the actual or predicted temperature T_(zone)between an minimum temperature bound T_(min) and a maximum temperaturebound T_(max) (i.e., T_(min)≤T_(zone)≤T_(max)) at all times. Theparameters T_(min) and T_(max) may be time-varying to define differenttemperature ranges at different times (e.g., an occupied temperaturerange, an unoccupied temperature range, a daytime temperature range, anighttime temperature range, etc.).

In order to ensure that the zone temperature constraint is satisfied,economic controller 510 can model the temperature T_(zone) of thebuilding zone as a function of the decision variables optimized byeconomic controller 510. In some embodiments, economic controller 510models the temperature of the building zone using a heat transfer model.For example, the dynamics of heating or cooling the building zone can bedescribed by the energy balance:

${C\frac{{dT}_{zone}}{dt}} = {{- {H\left( {T_{zone} - T_{a}} \right)}} + {\overset{.}{Q}}_{HVAC} + {\overset{.}{Q}}_{other}}$

where C is the thermal capacitance of the building zone, H is theambient heat transfer coefficient for the building zone, T_(zone) is thetemperature of the building zone, T_(a) is the ambient temperatureoutside the building zone (e.g., the outside air temperature), {dot over(Q)}_(HVAC) is the amount of heating applied to the building zone by CEF300, and {dot over (Q)}_(other) is the external load, radiation, orother disturbance experienced by the building zone. In the previousequation, {dot over (Q)}_(HVAC) represents heat transfer into thebuilding zone by CEF 300 (i.e., the heating load) and therefore has apositive sign. However, if cooling is applied to the building zonerather than heating, the sign on {dot over (Q)}_(HVAC) can be switchedto a negative sign such that {dot over (Q)}_(HVAC) represents the amountof cooling applied to the building zone by CEF 300 (i.e., the coolingload). Several techniques for developing zone temperature models andrelating the zone temperature T_(zone) to the decision variables in thepredictive cost function J are described in greater detail in U.S. Pat.No. 9,436,179 granted Sep. 6, 2016, U.S. patent application Ser. No.14/694,633 filed Apr. 23, 2015, and U.S. patent application Ser. No.15/199,910 filed Jun. 30, 2016. The entire disclosure of each of thesepatents and patent applications is incorporated by reference herein.

The previous energy balance combines all mass and air properties of thebuilding zone into a single zone temperature. Other heat transfer modelswhich can be used by economic controller 510 include the following airand mass zone models:

${C_{z}\frac{{dT}_{zone}}{dt}} = {{H_{az}\left( {T_{a} - T_{zone}} \right)} + {H_{mz}\left( {T_{m} - T_{zone}} \right)} + {\overset{.}{Q}}_{HVAC} + {\overset{.}{Q}}_{other}}$${C_{m}\frac{{dT}_{m}}{dt}} = {H_{mz}\left( {T_{zone} - T_{m}} \right)}$

where C_(z) and T_(zone) are the thermal capacitance and temperature ofthe air in the building zone, T_(a) is the ambient air temperature,H_(az) is the heat transfer coefficient between the air of the buildingzone and ambient air outside the building zone (e.g., through externalwalls of the building zone), C_(m) and T_(m) are the thermal capacitanceand temperature of the non-air mass within the building zone, and H_(mz)is the heat transfer coefficient between the air of the building zoneand the non-air mass.

The previous equation combines all mass properties of the building zoneinto a single zone mass. Other heat transfer models which can be used byeconomic controller 510 include the following air, shallow mass, anddeep mass zone models:

${C_{z}\frac{{dT}_{zone}}{dt}} = {{H_{az}\left( {T_{a} - T_{zone}} \right)} + {H_{sz}\left( {T_{s} - T_{zone}} \right)} + {\overset{.}{Q}}_{HVAC} + {\overset{.}{Q}}_{other}}$${C_{s}\frac{{dT}_{s}}{dt}} = {{H_{sz}\left( {T_{zone} - T_{s}} \right)} + {H_{ds}\left( {T_{d} - T_{s}} \right)}}$${C_{d}\frac{{dT}_{d}}{dt}} = {H_{ds}\left( {T_{s} - T_{d}} \right)}$

where C_(z) and T_(zone) are the thermal capacitance and temperature ofthe air in the building zone, T_(a) is the ambient air temperature,H_(az) is the heat transfer coefficient between the air of the buildingzone and ambient air outside the building zone (e.g., through externalwalls of the building zone), C_(s) and T_(s) are the thermal capacitanceand temperature of the shallow mass within the building zone, H_(sz) isthe heat transfer coefficient between the air of the building zone andthe shallow mass, C_(d) and T_(d) are the thermal capacitance andtemperature of the deep mass within the building zone, and H_(ds) is theheat transfer coefficient between the shallow mass and the deep mass.

In some embodiments, economic controller 510 uses the weather forecastsfrom weather service 416 to determine appropriate values for the ambientair temperature T_(a) and/or the external disturbance {dot over(Q)}_(other) at each time step of the optimization period. Values of Cand H can be specified as parameters of the building zone, received fromtracking controller 512, received from a user, retrieved from memory508, or otherwise provided as an input to economic controller 510.Accordingly, the temperature of the building zone T_(zone) can bedefined as a function of the amount of heating or cooling {dot over(Q)}_(HVAC) applied to the building zone by CEF 300 using any of theseheat transfer models. The manipulated variable {dot over (Q)}_(HVAC) canbe adjusted by economic controller 510 by adjusting the variablesP_(chiller), P_(HRC), F_(gas) and/or P_(total) in the predictive costfunction J.

In some embodiments, economic controller 510 uses a model that definesthe amount of heating or cooling {dot over (Q)}_(HVAC) applied to thebuilding zone by CEF 300 as a function of the power setpointsP_(sp,grid) and P_(sp,bat) provided by economic controller 510. Forexample, economic controller 510 can add the power setpoints P_(sp,grid)and P_(sp,bat) to determine the total amount of power P_(total) thatwill be consumed by powered CEF components 402. Economic controller 510can use P_(total) to determine the total amount of heating or cooling{dot over (Q)}_(HVAC) applied to the building zone by CEF 300.

In some embodiments, economic controller 510 uses one or more modelsthat define the amount of heating or cooling applied to the buildingzone by CEF 300 (i.e., {dot over (Q)}_(HVAC)) as a function of the zonetemperature T_(zone) and the zone temperature setpoint T_(sp,zone) asshown in the following equation:

{dot over (Q)} _(HVAC)=ƒ(T _(zone) ,T _(sp,zone))

The models used by economic controller 510 can be imposed asoptimization constraints to ensure that the amount of heating or cooling{dot over (Q)}_(HVAC) provided by CEF 300 is not reduced to a value thatwould cause the zone temperature T_(zone) to deviate from an acceptableor comfortable temperature range.

In some embodiments, economic controller 510 relates the amount ofheating or cooling {dot over (Q)}_(HVAC) provided by CEF 300 to the zonetemperature T_(zone) and the zone temperature setpoint T_(sp,zone) usingmultiple models. For example, economic controller 510 can use a model ofequipment controller 514 to determine the control action performed byequipment controller 514 as a function of the zone temperature T_(zone)and the zone temperature setpoint T_(sp,zone). An example of such a zoneregulatory controller model is shown in the following equation:

v _(air)=ƒ₁(T _(zone) ,T _(sp,zone))

where v_(air) is the rate of airflow to the building zone (i.e., thecontrol action). The function ƒ₁ can be identified from data. Forexample, economic controller 510 can collect measurements of v_(air) andT_(zone) and identify the corresponding value of T_(sp,zone). Economiccontroller 510 can perform a system identification process using thecollected values of v_(air), T_(zone), and T_(sp,zone) as training datato determine the function ƒ₁ that defines the relationship between suchvariables.

Economic controller 510 can use an energy balance model relating thecontrol action v_(air) to the amount of heating or cooling {dot over(Q)}_(HVAC) provided by CEF 300 as shown in the following equation:

{dot over (Q)} _(HVAC)=ƒ₂(v _(air))

where the function ƒ₂ can be identified from training data. Economiccontroller 510 can perform a system identification process usingcollected values of v_(air) and {dot over (Q)}_(HVAC) to determine thefunction ƒ₂ that defines the relationship between such variables.

In some embodiments, a linear relationship exists between {dot over(Q)}_(HVAC) and v_(air). Assuming an ideal proportional-integral (PI)controller and a linear relationship between {dot over (Q)}_(HVAC) andv_(air), a simplified linear controller model can be used to define theamount of heating or cooling {dot over (Q)}_(HVAC) provided by CEF 300as a function of the zone temperature T_(zone) and the zone temperaturesetpoint T_(sp,zone). An example of such a model is shown in thefollowing equations:

$Q_{HVAC} = {Q_{ss} + {K_{c}\left\lbrack {ɛ + {\frac{1}{\tau_{I}}{\int_{0}^{t}{{ɛ\left( t^{\prime} \right)}{dt}^{\prime}}}}} \right\rbrack}}$ɛ = T_(sp, zone) − T_(zone)

where {dot over (Q)}_(ss) is the steady-state rate of heating or coolingrate, K_(c) is the scaled zone PI controller proportional gain, τ₁ isthe zone PI controller integral time, and ε is the setpoint error (i.e.,the difference between the zone temperature setpoint T_(sp,zone) and thezone temperature T_(zone)). Saturation can be represented by constraintson {dot over (Q)}_(HVAC). If a linear model is not sufficiently accurateto model equipment controller 514 and heat transfer in CEF 300, anonlinear heating/cooling duty model can be used instead.

In addition to constraints on the zone temperature T_(zone), economiccontroller 510 can impose constraints on the state-of-charge (SOC) andcharge/discharge rates of battery unit 302. In some embodiments,economic controller 510 generates and imposes the following powerconstraints on the predictive cost function J:

P _(bat) ≤P _(rated) −P _(bat) ≤P _(rated)

where P_(bat) is the amount of power discharged from battery unit 302and P_(rated) is the rated battery power of battery unit 302 (e.g., themaximum rate at which battery unit 302 can be charged or discharged).These power constraints ensure that battery unit 302 is not charged ordischarged at a rate that exceeds the maximum possible batterycharge/discharge rate P_(rated).

In some embodiments, economic controller 510 generates and imposes oneor more capacity constraints on the predictive cost function J Thecapacity constraints may be used to relate the battery power P_(bat)charged or discharged during each time step to the capacity and SOC ofbattery unit 302. The capacity constraints may ensure that the capacityof battery unit 302 is maintained within acceptable lower and upperbounds at each time step of the optimization period. In someembodiments, economic controller 510 generates the following capacityconstraints:

C _(a)(k)−P _(bat)(k)Δt≤C _(rated)

C _(a)(k)−P _(bat)(k)Δt≥0

where C_(a)(k) is the available battery capacity (e.g., kWh) at thebeginning of time step k, P_(bat)(k) is the rate at which battery unit302 is discharged during time step k (e.g., kW), Δt is the duration ofeach time step, and C_(rated) is the maximum rated capacity of batteryunit 302 (e.g., kWh). The term P_(bat)(k)Δt represents the change inbattery capacity during time step k. These capacity constraints ensurethat the capacity of battery unit 302 is maintained between zero and themaximum rated capacity C_(rated).

In some embodiments, economic controller 510 generates and imposes oneor more capacity constraints on the operation of powered CEF components402. For example, powered CEF components 402 may have a maximumoperating point (e.g., a maximum pump speed, a maximum cooling capacity,etc.) which corresponds to a maximum power consumption P_(total,max).Economic controller 510 can be configured to generate a constraint whichlimits the power P_(total) provided to powered CEF components 402between zero and the maximum power consumption P_(total,max) as shown inthe following equation:

0≤P _(total) ≤P _(total,max)

P _(total) =P _(sp,grid) +P _(sp,bat)

where the total power P_(total) provided to powered CEF components 402is the sum of the grid power setpoint P_(sp,grid) and the battery powersetpoint P_(sp,bat).

In some embodiments, economic controller 510 generates and imposes oneor more capacity constraints on the operation of the one or moresubplants of CEF 300. For example, heating may be provided by heatersubplant 202 and cooling may be provided by chiller subplant 206. Theoperation of heater subplant 202 and chiller subplant 206 may be definedby subplant curves for each of heater subplant 202 and chiller subplant206. Each subplant curve may define the resource production of thesubplant (e.g., tons refrigeration, kW heating, etc.) as a function ofone or more resources consumed by the subplant (e.g., electricity,natural gas, water, etc.). Several examples of subplant curves which canbe used by economic controller 510 are described in greater detail inU.S. patent application Ser. No. 14/634,609 filed Feb. 27, 2015.

Economic controller 510 can be configured to use the subplant curves toidentify a maximum amount of heating that can be provided by heatersubplant 202 and a maximum amount of cooling that can be provided bychiller subplant 206. Economic controller 510 can generate and impose aconstraint that limits the amount of heating provided by heater subplant202 between zero and the maximum amount of heating. Similarly, economiccontroller 510 can generate and impose a constraint that limits theamount of cooling provided by chiller subplant 206 between zero and themaximum amount of cooling.

Economic controller 510 can optimize the predictive cost function Jsubject to the constraints to determine optimal values for the decisionvariables P_(total), P_(chiller), P_(HRC), F_(gas), P_(grid), andP_(bat), where P_(total)=P_(bat)+P_(grid)+P_(PV). In some embodiments,economic controller 510 uses the optimal values for P_(total), P_(bat),and/or P_(grid) to generate power setpoints for tracking controller 512.The power setpoints can include battery power setpoints P_(sp,bat), gridpower setpoints P_(sp,grid), and/or CEF power setpoints P_(sp,total) foreach of the time steps k in the optimization period. Economic controller510 can provide the power setpoints to tracking controller 512.

Tracking Controller

Tracking controller 512 can use the optimal power setpoints P_(sp,grid),P_(sp,bat), and/or P_(sp,total) generated by economic controller 510 todetermine optimal temperature setpoints (e.g., a zone temperaturesetpoint T_(sp,zone,) a chilled water temperature setpoint T_(sp,chw),etc.) and an optimal battery charge or discharge rate (i.e., Bat_(C/D)).In some embodiments, tracking controller 512 generates a zonetemperature setpoint T_(sp,zone) and/or a chilled water temperaturesetpoint T_(sp,chw) that are predicted to achieve the power setpointP_(sp,total) for CEF 300. In other words, tracking controller 512 maygenerate a zone temperature setpoint T_(sp,zone) and/or a chilled watertemperature setpoint T_(sp,chw) that cause CEF 300 to consume theoptimal amount of power P_(total) determined by economic controller 510.

In some embodiments, tracking controller 512 relates the powerconsumption of CEF 300 to the zone temperature T_(zone) and the zonetemperature setpoint T_(sp,zone) using a power consumption model. Forexample, tracking controller 512 can use a model of equipment controller514 to determine the control action performed by equipment controller514 as a function of the zone temperature T_(zone) and the zonetemperature setpoint T_(sp,zone). An example of such a zone regulatorycontroller model is shown in the following equation:

v _(air)=ƒ₃(T _(zone) ,T _(sp,zone))

where v_(air) is the rate of airflow to the building zone (i.e., thecontrol action).

Tracking controller 512 can define the power consumption P_(total) ofCEF 300 as a function of the zone temperature T_(zone) and the zonetemperature setpoint T_(sp,zone). An example of such a model is shown inthe following equation:

P _(total)=ƒ₄(T _(zone) ,T _(sp,zone))

The function ƒ₄ can be identified from data. For example, trackingcontroller 512 can collect measurements of P_(total) and T_(zone) andidentify the corresponding value of T_(sp,zone). Tracking controller 512can perform a system identification process using the collected valuesof P_(total), T_(zone), and T_(sp,zone) as training data to determinethe function ƒ₄ that defines the relationship between such variables.

Tracking controller 512 may use a similar model to determine therelationship between the total power consumption P_(total) of CEF 300and the chilled water temperature setpoint T_(sp,chw). For example,tracking controller 512 can define the power consumption P_(total) ofCEF 300 as a function of the zone temperature T_(zone) and the chilledwater temperature setpoint T_(sp,chw). An example of such a model isshown in the following equation:

P _(total)=ƒ₅(T _(zone) ,T _(sp,chw))

The function ƒ₅ can be identified from data. For example, trackingcontroller 512 can collect measurements of P_(total) and T_(zone) andidentify the corresponding value of T_(sp,chw). Tracking controller 512can perform a system identification process using the collected valuesof P_(total), T_(zone), and T_(sp,chw) as training data to determine thefunction ƒ₅ that defines the relationship between such variables.

Tracking controller 512 can use the relationships between P_(total),T_(sp,zone), and T_(sp,chw) to determine values for T_(sp,zone) andT_(sp,chw). For example, tracking controller 512 can receive the valueof P_(total) as an input from economic controller 510 (i.e.,P_(sp,total)) and can use determine corresponding values of T_(sp,zone)and T_(sp,chw). Tracking controller 512 can provide the values ofT_(sp,zone) and T_(sp,chw) as outputs to equipment controller 514.

In some embodiments, tracking controller 512 uses the battery powersetpoint P_(sp,bat) to determine the optimal rate Bat_(C/D) at which tocharge or discharge battery unit 302. For example, the battery powersetpoint P_(sp,bat) may define a power value (kW) which can betranslated by tracking controller 512 into a control signal for powerinverter 410 and/or equipment controller 514. In other embodiments, thebattery power setpoint P_(sp,bat) is provided directly to power inverter410 and used by power inverter 410 to control the battery power P_(bat).

Equipment Controller

Equipment controller 514 can use the optimal temperature setpointsT_(sp,zone) or T_(sp,chw) generated by tracking controller 512 togenerate control signals for powered CEF components 402. The controlsignals generated by equipment controller 514 may drive the actual(e.g., measured) temperatures T_(zone) and/or T_(chw), to the setpoints.Equipment controller 514 can use any of a variety of control techniquesto generate control signals for powered CEF components 402. For example,equipment controller 514 can use state-based algorithms, extremumseeking control (ESC) algorithms, proportional-integral (PI) controlalgorithms, proportional-integral-derivative (PID) control algorithms,model predictive control (MPC) algorithms, or other feedback controlalgorithms, to generate control signals for powered CEF components 402.

The control signals may include on/off commands, speed setpoints forfans of cooling towers 404, power setpoints for compressors of chillers406, chilled water temperature setpoints for chillers 406, pressuresetpoints or flow rate setpoints for pumps 408, or other types ofsetpoints for individual devices of powered CEF components 402. In otherembodiments, the control signals may include the temperature setpoints(e.g., a zone temperature setpoint T_(sp,zone), a chilled watertemperature setpoint T_(sp,chw), etc.) generated by predictive CEFcontroller 304. The temperature setpoints can be provided to powered CEFcomponents 402 or local controllers for powered CEF components 402 whichoperate to achieve the temperature setpoints. For example, a localcontroller for chillers 406 may receive a measurement of the chilledwater temperature T_(chw) from chilled water temperature sensor and/or ameasurement the zone temperature T_(zone) from a zone temperaturesensor.

In some embodiments, equipment controller 514 is configured to providecontrol signals to power inverter 410. The control signals provided topower inverter 410 can include a battery power setpoint P_(sp,bat)and/or the optimal charge/discharge rate Bat_(C/D). Equipment controller514 can be configured to operate power inverter 410 to achieve thebattery power setpoint P_(sp,bat). For example, equipment controller 514can cause power inverter 410 to charge battery unit 302 or dischargebattery unit 302 in accordance with the battery power setpointP_(sp,bat).

Referring now to FIG. 6, a user interface 600 which can be generated bypredictive CEF controller 304 is shown, according to some embodiments.As discussed above, economic controller 510 can be configured todetermine the portion of each power consumption value (e.g.,P_(chiller), P_(HRC), etc.) that consists of grid power and/or batterypower at each time step of the optimization period. User interface 600can be used to convey to a user the relative portions of each powerconsumption value that consist of grid power and/or battery power.

Interface 600 illustrates a dispatch chart. The top half of the dispatchchart corresponds to cooling, whereas the bottom half of the dispatchchart corresponds to heating. The midline between the top and bottomhalves corresponds to zero load/power for both halves. Positive coolingvalues are shown as displacement above the midline, whereas positiveheating values are shown as displacement below the midline. Lines 602and 612 represent the requested cooling load and the requested heatingload, respectively, at each time step of the optimization period. Lines604 and 614 represent the charge level of batteries used to power thecooling equipment (e.g., a chiller subplant) and the heating equipment(e.g., a heater subplant) over the duration of the optimization period.

As discussed above, economic controller 510 can be configured todetermine optimal power setpoints for each time step of the optimizationperiod. The results of the optimization performed by economic controller510 can be represented in the dispatch chart. For example, the dispatchchart is shown to include a vertical column for each time step of theoptimization period. Each column may include one or more barsrepresenting the power setpoints determined by economic controller 510for the corresponding time step. The color of each bar indicates thetype of power setpoint. For example, gray bars 608 and 618 (shown aswhite bars in FIG. 6) may indicate the grid power setpoint (e.g.,P_(sp,grid)) whereas green bars 606 and 616 (shown as shaded bars inFIG. 6) may indicate the battery power setpoint (e.g., P_(sp,bat)). Theheight of each bar indicates the magnitude of the corresponding powersetpoint at that time step.

Green bars 606 positioned above requested cooling line 602 indicate thatthe cooling equipment battery is charging (i.e., excess energy used tocharge the battery), whereas green bars 606 positioned below requestedcooling line 602 indicate that the cooling equipment battery isdischarging (i.e., battery power used to satisfy part of the requestedcooling load). The charge level of the cooling equipment batteryincreases when the cooling equipment battery is charging and decreaseswhen the cooling equipment battery is discharging.

Similarly, green bars 616 positioned below requested heating line 612indicate that the heating equipment battery is charging (i.e., excessenergy used to charge the battery), whereas green bars 616 positionedabove requested heating line 612 indicate that the heating equipmentbattery is discharging (i.e., battery power used to satisfy part of therequested heating load). The charge level of the heating equipmentbattery increases when the heating equipment battery is charging anddecreases when the heating equipment battery is discharging.

Air Cooled Chiller with Battery Unit and Predictive Control

Referring now to FIGS. 7-8, an air-cooled chiller 700 with a batteryunit 702 and predictive chiller controller 704 is shown, according tosome embodiments. Chiller 700 can be configured to provide a chilledfluid (e.g., chilled water 718) to a cooling load 734 via chilled waterpipe 714. Cooling load 734 can include, for example, a building zone, asupply airstream flowing through an air duct, an airflow in an airhandling unit or rooftop unit, fluid flowing through a heat exchanger, arefrigerator or freezer, a condenser or evaporator, a cooling coil, orany other type of system, device, or space which requires cooling. Insome embodiments, a pump 732 circulates a chilled fluid to cooling load734 via a chilled fluid circuit 738. The chilled fluid can absorb heatfrom cooling load 734, thereby providing cooling to cooling load 734 andwarming the chilled fluid. The warmed fluid (shown in FIG. 7 as returnwater 716) may return to chiller 700 via return water pipe 712.

Chiller 700 is shown to include a condenser 722, a compressor 720, anevaporator 724, an expansion device 726, and a fan 730. Compressor 720can be configured to circulate a refrigerant between condenser 722 andevaporator 724 via refrigeration circuit 736. Compressor 720 operates tocompress the refrigerant to a high pressure, high temperature state. Thecompressed refrigerant flows through condenser 722, which transfers heatfrom the refrigerant in refrigeration circuit 736 to an airflow 728. Afan 730 can be used to force airflow 728 through or over condenser 722to provide cooling for the refrigerant in condenser 722. The cooledrefrigerant then flows through expansion device 726, which expands therefrigerant to a low temperature, low pressure state. The expandedrefrigerant flows through evaporator 724, which transfers heat from thechilled fluid in chilled fluid circuit 738 to the refrigerant inrefrigeration circuit 736.

In some embodiments, chiller 700 includes one or more photovoltaic (PV)panels 708. PV panels 708 may include a collection of photovoltaiccells. The photovoltaic cells are configured to convert solar energy(i.e., sunlight) into electricity using a photovoltaic material such asmonocrystalline silicon, polycrystalline silicon, amorphous silicon,cadmium telluride, copper indium gallium selenide/sulfide, or othermaterials that exhibit the photovoltaic effect. In some embodiments, thephotovoltaic cells are contained within packaged assemblies that form PVpanels 708. Each PV panel 708 may include a plurality of linkedphotovoltaic cells. PV panels 708 may combine to form a photovoltaicarray.

In some embodiments, PV panels 708 are configured to maximize solarenergy collection. For example, chiller 700 may include a solar tracker(e.g., a GPS tracker, a sunlight sensor, etc.) that adjusts the angle ofPV panels 708 so that PV panels 708 are aimed directly at the sunthroughout the day. The solar tracker may allow PV panels 708 to receivedirect sunlight for a greater portion of the day and may increase thetotal amount of power produced by PV panels 708. In some embodiments,chiller 700 includes a collection of mirrors, lenses, or solarconcentrators configured to direct and/or concentrate sunlight on PVpanels 708. The energy generated by PV panels 708 may be stored inbattery unit 702 and/or used to power various components of chiller 700.

In some embodiments, battery unit 702 includes one or more battery cells706. Battery cells 706 are configured to store and discharge electricenergy (i.e., electricity). In some embodiments, battery unit 702 ischarged using electricity from an external energy grid (e.g., providedby an electric utility). The electricity stored in battery unit 702 canbe discharged to power one or more powered components of chiller 700(e.g., fan 730, compressor 720, pump 732, etc.). Advantageously, batteryunit 702 allows chiller 700 to draw electricity from the energy grid andcharge battery unit 702 when energy prices are low and discharge thestored electricity when energy prices are high to time-shift theelectric load of chiller 700. In some embodiments, battery unit 702 hassufficient energy capacity to power chiller 700 for approximately 4-6hours when operating at maximum capacity such that battery unit 702 canbe utilized during high energy cost periods and charged during lowenergy cost periods.

In some embodiments, predictive chiller controller 704 performs anoptimization process to determine whether to charge or discharge batteryunit 702 during each of a plurality of time steps that occur during anoptimization period. Predictive chiller controller 704 may use weatherand pricing data 710 to predict the amount of heating/cooling requiredand the cost of electricity during each of the plurality of time steps.Predictive chiller controller 704 can optimize an objective functionthat accounts for the cost of electricity purchased from the energy gridover the duration of the optimization period. Predictive chillercontroller 704 can determine an amount of electricity to purchase fromthe energy grid and an amount of electricity to store or discharge frombattery unit 702 during each time step. The objective function and theoptimization performed by predictive chiller controller 704 aredescribed in greater detail with reference to FIGS. 9-10.

Predictive Chiller Control System

Referring now to FIG. 9, a block diagram of a predictive chiller controlsystem 900 is shown, according to some embodiments. Several of thecomponents shown in control system 900 may be part of chiller 700. Forexample, chiller 700 may include powered chiller components 902, batteryunit 702, predictive chiller controller 704, power inverter 910, and apower junction 912. Powered chiller components 902 may include anycomponent of chiller 700 that consumes power (e.g., electricity) duringoperation. For example, powered chiller components 902 are shown toinclude cooling fan 730, compressor 720, and pump 732.

Power inverter 910 may be configured to convert electric power betweendirect current (DC) and alternating current (AC). For example, batteryunit 702 may be configured to store and output DC power, whereas energygrid 914 and powered chiller components 902 may be configured to consumeand provide AC power. Power inverter 910 may be used to convert DC powerfrom battery unit 702 into a sinusoidal AC output synchronized to thegrid frequency of energy grid 914 and/or powered chiller components 902.Power inverter 910 may also be used to convert AC power from energy grid914 into DC power that can be stored in battery unit 702. The poweroutput of battery unit 702 is shown as P_(bat). P_(bat) may be positiveif battery unit 702 is providing power to power inverter 910 (i.e.,battery unit 702 is discharging) or negative if battery unit 702 isreceiving power from power inverter 910 (i.e., battery unit 702 ischarging).

In some instances, power inverter 910 receives a DC power output frombattery unit 702 and converts the DC power output to an AC power outputthat can be provided to powered chiller components 902. Power inverter910 may synchronize the frequency of the AC power output with that ofenergy grid 914 (e.g., 50 Hz or 60 Hz) using a local oscillator and maylimit the voltage of the AC power output to no higher than the gridvoltage. In some embodiments, power inverter 910 is a resonant inverterthat includes or uses LC circuits to remove the harmonics from a simplesquare wave in order to achieve a sine wave matching the frequency ofenergy grid 914. In various embodiments, power inverter 910 may operateusing high-frequency transformers, low-frequency transformers, orwithout transformers. Low-frequency transformers may convert the DCoutput from battery unit 702 directly to the AC output provided topowered chiller components 902. High-frequency transformers may employ amulti-step process that involves converting the DC output tohigh-frequency AC, then back to DC, and then finally to the AC outputprovided to powered chiller components 902.

The power output of PV panels 708 is shown as P_(PV). The power outputP_(PV) of PV panels 708 can be stored in battery unit 702 and/or used topower powered chiller components 902. In some embodiments, PV panels 708measure the amount of power P_(PV) generated by PV panels 708 andprovides an indication of the PV power to predictive chiller controller704. For example, PV panels 708 are shown providing an indication of thePV power percentage (i.e., PV %) to predictive chiller controller 704.The PV power percentage may represent a percentage of the maximum PVpower at which PV panels 708 are currently operating.

Power junction 912 is the point at which powered chiller components 902,energy grid 914, PV panels 708, and power inverter 910 are electricallyconnected. The power supplied to power junction 912 from power inverter910 is shown as P_(bat). P_(bat) may be positive if power inverter 910is providing power to power junction 912 (i.e., battery unit 702 isdischarging) or negative if power inverter 910 is receiving power frompower junction 912 (i.e., battery unit 702 is charging). The powersupplied to power junction 912 from energy grid 914 is shown as P_(grid)and the power supplied to power junction 912 from PV panels 708 is shownas P_(PV). P_(bat), P_(PV), and P_(grid) combine at power junction 912to form P_(total) (i.e. P_(total)=P_(grid)+P_(bat)+P_(PV)). P_(total)may be defined as the power provided to powered chiller components 902from power junction 912. In some instances, P_(total) is greater thanP_(grid). For example, when battery unit 702 is discharging, P_(bat) maybe positive which adds to the grid power P_(grid) and the PV powerP_(PV) when P_(bat) and P_(PV) combine with P_(grid) to form P_(total).In other instances, P_(total) may be less than P_(grid). For example,when battery unit 702 is charging, P_(bat) may be negative whichsubtracts from the grid power P_(grid) and the PV power P_(PV) whenP_(bat), P_(PV), and P_(grid) combine to form P_(total).

Predictive chiller controller 704 can be configured to control poweredchiller components 902 and power inverter 910. In some embodiments,predictive chiller controller 704 generates and provides a battery powersetpoint P_(sp,bat) to power inverter 910. The battery power setpointP_(sp,bat) may include a positive or negative power value (e.g., kW)which causes power inverter 910 to charge battery unit 702 (whenP_(sp,bat) is negative) using power available at power junction 912 ordischarge battery unit 702 (when P_(sp,bat) is positive) to providepower to power junction 912 in order to achieve the battery powersetpoint P_(sp,bat).

In some embodiments, predictive chiller controller 704 generates andprovides control signals to powered chiller components 902. Predictivechiller controller 704 may use a multi-stage optimization technique togenerate the control signals. For example, predictive chiller controller704 may include an economic controller configured to determine theoptimal amount of power to be consumed by powered chiller components 902at each time step during the optimization period. The optimal amount ofpower to be consumed may minimize a cost function that accounts for thecost of energy consumed by chiller 700. The cost of energy may be basedon time-varying energy prices from electric utility 918. In someembodiments, predictive chiller controller 704 determines an optimalamount of power to purchase from energy grid 914 (i.e., a grid powersetpoint P_(sp,grid)) and an optimal amount of power to store ordischarge from battery unit 702 (i.e., a battery power setpointP_(sp,bat)) at each of the plurality of time steps. Predictive chillercontroller 704 may monitor the actual power usage of powered chillercomponents 902 and may utilize the actual power usage as a feedbacksignal when generating the optimal power setpoints.

Predictive chiller controller 704 may include a tracking controllerconfigured to generate temperature setpoints (e.g., an air temperaturesetpoint T_(sp,air), a chilled water temperature setpoint T_(sp,water),etc.) that achieve the optimal amount of power consumption at each timestep. In some embodiments, predictive chiller controller 704 usesequipment models for powered chiller components 902 to determine anamount of heating or cooling that can be generated by chiller components902 based on the optimal amount of power consumption. Predictive chillercontroller 704 can use a temperature model to predict how thetemperature of the chilled water T_(water) will change based on thepower setpoints.

In some embodiments, predictive chiller controller 704 uses thetemperature setpoints to generate the control signals for poweredchiller components 902. The control signals may include on/off commands,speed setpoints for fan 730, power setpoints for compressor 720, chilledwater temperature setpoints chiller 700, pressure setpoints or flow ratesetpoints for pump 732, or other types of setpoints for individualdevices of powered chiller components 902. In other embodiments, thecontrol signals may include the temperature setpoints (e.g., an airtemperature setpoint T_(sp,air,) a chilled water temperature setpointT_(sp,water), etc.) generated by predictive chiller controller 704. Thetemperature setpoints can be provided to powered chiller components 902or local controllers for powered chiller components 902 which operate toachieve the temperature setpoints. For example, a local controller forfan 730 may receive a measurement of the chilled water temperatureT_(water) from a chilled water temperature sensor and/or a measurementthe air temperature T_(air) (i.e., the temperature of airflow 728) froman air temperature sensor. The local controller can use a feedbackcontrol process (e.g., PID, ESC, MPC, etc.) to increase or decrease theairflow provided by fan 730 to drive the measured temperature(s) to thetemperature setpoint(s). Similar feedback control processes can be usedto compressor 720 and/or pump 732. The multi-stage optimizationperformed by predictive chiller controller 704 is described in greaterdetail with reference to FIG. 10.

Predictive Chiller Controller

Referring now to FIG. 10, a block diagram illustrating predictivechiller controller 704 in greater detail is shown, according to anexemplary embodiment. Predictive chiller controller 704 is shown toinclude a communications interface 1002 and a processing circuit 1004.Communications interface 1002 may facilitate communications betweencontroller 704 and external systems or devices. For example,communications interface 1002 may receive measurements of the airtemperature T_(air) and the chilled water temperature T_(water) fromtemperature sensors 1016 and measurements of the power usage of poweredchiller components 902. In some embodiments, communications interface1002 receives measurements of the state-of-charge (SOC) of battery unit702, which can be provided as a percentage of the maximum batterycapacity (i.e., battery %). Communications interface 1002 can receiveweather forecasts from a weather service 916 and predicted energy costsand demand costs from an electric utility 918. In some embodiments,predictive chiller controller 704 uses communications interface 1002 toprovide control signals powered chiller components 902 and powerinverter 910.

Communications interface 1002 may include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications external systems or devices. In various embodiments, thecommunications may be direct (e.g., local wired or wirelesscommunications) or via a communications network (e.g., a WAN, theInternet, a cellular network, etc.). For example, communicationsinterface 1002 can include an Ethernet card and port for sending andreceiving data via an Ethernet-based communications link or network. Inanother example, communications interface 1002 can include a Wi-Fitransceiver for communicating via a wireless communications network orcellular or mobile phone communications transceivers.

Processing circuit 1004 is shown to include a processor 1006 and memory1008. Processor 1006 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 1006 isconfigured to execute computer code or instructions stored in memory1008 or received from other computer readable media (e.g., CDROM,network storage, a remote server, etc.).

Memory 1008 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 1008 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory1008 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 1008 may be communicably connected toprocessor 1006 via processing circuit 1004 and may include computer codefor executing (e.g., by processor 1006) one or more processes describedherein. When processor 1006 executes instructions stored in memory 1008for completing the various activities described herein, processor 1006generally configures controller 704 (and more particularly processingcircuit 1004) to complete such activities.

Still referring to FIG. 10, predictive chiller controller 704 is shownto include an economic controller 1010, a tracking controller 1012, andan equipment controller 1014. Controllers 1010-1014 can be configured toperform a multi-state optimization process to generate control signalsfor power inverter 910 and powered chiller components 902. In briefoverview, economic controller 1010 can optimize a predictive costfunction to determine an optimal amount of power to purchase from energygrid 914 (i.e., a grid power setpoint P_(sp,grid)), an optimal amount ofpower to store or discharge from battery unit 702 (i.e., a battery powersetpoint P_(sp,bat)), and/or an optimal amount of power to be consumedby powered chiller components 902 (i.e., a chiller power setpointP_(sp,total)) at each time step of an optimization period. Trackingcontroller 1012 can use the optimal power setpoints P_(sp,grid),P_(sp,bat), and/or P_(sp,total) to determine optimal temperaturesetpoints (e.g., an air setpoint T_(sp,air), a chilled water temperaturesetpoint T_(sp,water), etc.) and an optimal battery charge or dischargerate (i.e., Bat_(C/D)). Equipment controller 1014 can use the optimaltemperature setpoints T_(sp,air) or T_(sp,water) to generate controlsignals for powered chiller components 902 that drive the actual (e.g.,measured) temperatures T_(air) and/or T_(water) to the setpoints (e.g.,using a feedback control technique). Each of controllers 1010-1014 isdescribed in detail below.

Economic Controller

Economic controller 1010 can be configured to optimize a predictive costfunction to determine an optimal amount of power to purchase from energygrid 914 (i.e., a grid power setpoint P_(sp,grid)), an optimal amount ofpower to store or discharge from battery unit 702 (i.e., a battery powersetpoint P_(sp,bat)), and/or an optimal amount of power to be consumedby powered chiller components 902 (i.e., a chiller power setpointP_(sp,total)) at each time step of an optimization period. An example ofa predictive cost function which can be optimized by economic controller1010 is shown in the following equation:

${\min (J)} = {{\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{fan}(k)}\Delta \; t}} + {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{comp}(k)}\Delta \; t}} + {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{pump}(k)}\Delta \; t}} + {C_{DC}{\max\limits_{k}\left( {P_{grid}(k)} \right)}} - {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{bat}(k)}\Delta \; t}}}$

where C_(ec)(k) is the cost per unit of electricity (e.g., $/kWh)purchased from electric utility 918 during time step k, P_(fan)(k) isthe power consumption (e.g., kW) of fan 730 during time step k,P_(comp)(k) is the power consumption of compressor 720 at time step k,P_(pump) (k) is the power consumption of pump 732 at time step k, C_(DC)is the demand charge rate (e.g., $/kW), where the max( ) term selectsthe maximum electricity purchase of chiller 700 (i.e., the maximum valueof P_(grid)(k)) during any time step k of the optimization period,P_(bat)(k) is the amount of power discharged from battery unit 702during time step k, and Δt is the duration of each time step k. Economiccontroller 1010 can optimize the predictive cost function J over theduration of the optimization period (e.g., from time step k=1 to timestep k=h) to predict the total cost of operating chiller 700 over theduration of the optimization period.

The first, second, and third terms of the predictive cost function Jrepresent the cost of electricity consumed by powered chiller components902 over the duration of the optimization period. The values of theparameter C_(ec)(k) at each time step k can be defined by the energycost information provided by electric utility 918. In some embodiments,the cost of electricity varies as a function of time, which results indifferent values of C_(ec)(k) at different time steps k. The variablesP_(fan)(k), P_(comp)(k), and P_(pump)(k) are decision variables whichcan be optimized by economic controller 1010. In some embodiments, thetotal power consumption P_(total)(k) of powered chiller components 902at time step k is equal to the sum of P_(fan)(k), P_(comp)(k), andP_(pump)(k) (i.e., P_(total)(k)=P_(fan)(k)+P_(comp)(k)+P_(pump)(k)).Accordingly, the first three terms of the predictive cost function canbe replaced with the summation Σ_(k=1) ^(h)C_(ec)(k)P_(total)(k)Δt insome embodiments.

The fourth term of the predictive cost function J represents the demandcharge. Demand charge is an additional charge imposed by some utilityproviders based on the maximum power consumption during an applicabledemand charge period. For example, the demand charge rate C_(DC) may bespecified in terms of dollars per unit of power (e.g., $/kW) and may bemultiplied by the peak power usage (e.g., kW) during a demand chargeperiod to calculate the demand charge. In the predictive cost functionJ, the demand charge rate C_(DC) may be defined by the demand costinformation received from electric utility 918. The variable P_(grid)(k)is a decision variable which can be optimized by economic controller1010 in order to reduce the peak power usage max(P_(grid)(k)) thatoccurs during the demand charge period. Load shifting may allow economiccontroller 1010 to smooth momentary spikes in the electric demand ofchiller 700 by storing energy in battery unit 702 when the powerconsumption of powered chiller components 902 is low. The stored energycan be discharged from battery unit 702 when the power consumption ofpowered chiller components 902 is high in order to reduce the peak powerdraw P_(grid) from energy grid 914, thereby decreasing the demand chargeincurred.

The final term of the predictive cost function J represents the costsavings resulting from the use of battery unit 702. Unlike the previousterms in the cost function J, the final term subtracts from the totalcost. The values of the parameter C_(ec)(k) at each time step k can bedefined by the energy cost information provided by electric utility 918.In some embodiments, the cost of electricity varies as a function oftime, which results in different values of C_(ec)(k) at different timesteps k. The variable P_(bat)(k) is a decision variable which can beoptimized by economic controller 1010. A positive value of P_(bat)(k)indicates that battery unit 702 is discharging, whereas a negative valueof P_(bat)(k) indicates that battery unit 702 is charging. The powerdischarged from battery unit 702 P_(bat)(k) can be used to satisfy someor all of the total power consumption P_(total)(k) of powered chillercomponents 902, which reduces the amount of power P_(grid)(k) purchasedfrom energy grid 914 (i.e.,P_(grid)(k)=P_(total)(k)−P_(bat)(k)−P_(PV)(k)). However, chargingbattery unit 702 results in a negative value of P_(bat)(k) which adds tothe total amount of power P_(grid)(k) purchased from energy grid 914.

In some embodiments, the power P_(PV) provided by PV panels 708 is notincluded in the predictive cost function J because generating PV powerdoes not incur a cost. However, the power P_(PV) generated by PV panels708 can be used to satisfy some or all of the total power consumptionP_(total)(k) of powered chiller components 902, which reduces the amountof power P_(grid)(k) purchased from energy grid 914 (i.e.,P_(grid)(k)=P_(total)(k)−P_(bat)(k)−P_(PV)(k)). The amount of PV powerP_(PV) generated during any time step k can be predicted by economiccontroller 1010. Several techniques for predicting the amount of PVpower generated by PV panels are described in U.S. patent applicationSer. No. 15/247,869, U.S. patent application Ser. No. 15/247,844, andU.S. patent application Ser. No. 15/247,788. Each of these patentapplications has a filing date of Aug. 25, 2016, and the entiredisclosure of each of these patent applications is incorporated byreference herein.

Economic controller 1010 can optimize the predictive cost function Jover the duration of the optimization period to determine optimal valuesof the decision variables at each time step during the optimizationperiod. In some embodiments, the optimization period has a duration ofapproximately one day and each time step is approximately fifteenminutes. However, the durations of the optimization period and the timesteps can vary in other embodiments and can be adjusted by a user.Advantageously, economic controller 1010 can use battery unit 702 toperform load shifting by drawing electricity from energy grid 914 whenenergy prices are low and/or when the power consumed by powered chillercomponents 902 is low. The electricity can be stored in battery unit 702and discharged later when energy prices are high and/or the powerconsumption of powered chiller components 902 is high. This enableseconomic controller 1010 to reduce the cost of electricity consumed bychiller 700 and can smooth momentary spikes in the electric demand ofchiller 700, thereby reducing the demand charge incurred.

Economic controller 1010 can be configured to impose constraints on theoptimization of the predictive cost function J. In some embodiments, theconstraints include constraints on the temperature T_(water) of thechilled water produced by chiller 700. Economic controller 1010 can beconfigured to maintain the actual or predicted temperature T_(water)between a minimum temperature bound T_(min) and a maximum temperaturebound T_(max) (i.e., T_(min)≤T_(water)≤T_(max)) at all times. Theparameters T_(min) and T_(max) may be time-varying to define differenttemperature ranges at different times.

In addition to constraints on the water temperature T_(water), economiccontroller 1010 can impose constraints on the state-of-charge (SOC) andcharge/discharge rates of battery unit 702. In some embodiments,economic controller 1010 generates and imposes the following powerconstraints on the predictive cost function J:

P _(bat) ≤P _(rated) −P _(bat) ≤P _(rated)

where P_(bat) is the amount of power discharged from battery unit 702and P_(rated) is the rated battery power of battery unit 702 (e.g., themaximum rate at which battery unit 702 can be charged or discharged).These power constraints ensure that battery unit 702 is not charged ordischarged at a rate that exceeds the maximum possible batterycharge/discharge rate P_(rated).

In some embodiments, economic controller 1010 generates and imposes oneor more capacity constraints on the predictive cost function J Thecapacity constraints may be used to relate the battery power P_(bat)charged or discharged during each time step to the capacity and SOC ofbattery unit 702. The capacity constraints may ensure that the capacityof battery unit 702 is maintained within acceptable lower and upperbounds at each time step of the optimization period. In someembodiments, economic controller 1010 generates the following capacityconstraints:

C _(a)(k)−P _(bat)(k)Δt≤C _(rated)

C _(a)(k)−P _(bat)(k)Δt≥0

where C_(a)(k) is the available battery capacity (e.g., kWh) at thebeginning of time step k, P_(bat)(k) is the rate at which battery unit702 is discharged during time step k (e.g., kW), Δt is the duration ofeach time step, and C_(rated) is the maximum rated capacity of batteryunit 702 (e.g., kWh). The term P_(bat)(k)Δt represents the change inbattery capacity during time step k. These capacity constraints ensurethat the capacity of battery unit 702 is maintained between zero and themaximum rated capacity C_(rated).

In some embodiments, economic controller 1010 generates and imposes oneor more capacity constraints on the operation of powered chillercomponents 902. For example, powered chiller components 902 may have amaximum operating point (e.g., a maximum pump speed, a maximum coolingcapacity, etc.) which corresponds to a maximum power consumptionP_(total,max). Economic controller 1010 can be configured to generate aconstraint which limits the power P_(total) provided to powered chillercomponents 902 between zero and the maximum power consumptionP_(total,max) as shown in the following equation:

0≤P _(total) ≤P _(total,max)

P_(total)=P_(sp,grid)+P_(sp,bat) where the total power P_(total)provided to powered chiller components 902 is the sum of the grid powersetpoint P_(sp,grid) and the battery power setpoint P_(sp,bat).

Economic controller 1010 can optimize the predictive cost function Jsubject to the constraints to determine optimal values for the decisionvariables P_(total), P_(fan), P_(comp), P_(pump), P_(grid), and P_(bat),where P_(total)=P_(bat)+P_(grid)+P_(PV). In some embodiments, economiccontroller 1010 uses the optimal values for P_(total), P_(bat), and/orP_(grid) to generate power setpoints for tracking controller 1012. Thepower setpoints can include battery power setpoints P_(sp,bat), gridpower setpoints P_(sp,grid), and/or chiller power setpoints P_(sp,total)for each of the time steps k in the optimization period. Economiccontroller 1010 can provide the power setpoints to tracking controller1012.

Tracking Controller

Tracking controller 1012 can use the optimal power setpointsP_(sp,grid), P_(sp,bat), and/or P_(sp,total) generated by economiccontroller 1010 to determine optimal temperature setpoints (e.g., an airtemperature setpoint T_(sp,air,) a chilled water temperature setpointT_(sp,water,) etc.) and an optimal battery charge or discharge rate(i.e., Bat_(C/D)). In some embodiments, tracking controller 1012generates an air temperature setpoint T_(sp,air) and/or a chilled watertemperature setpoint T_(sp,water) that are predicted to achieve thepower setpoint P_(sp,total) for chiller 700. In other words, trackingcontroller 1012 may generate an air temperature setpoint T_(sp,air)and/or a chilled water temperature setpoint T_(sp,water) that causechiller 700 to consume the optimal amount of power P_(total) determinedby economic controller 1010.

In some embodiments, tracking controller 1012 uses the battery powersetpoint P_(sp,bat) to determine the optimal rate Bat_(C/D) at which tocharge or discharge battery unit 702. For example, the battery powersetpoint P_(sp,bat) may define a power value (kW) which can betranslated by tracking controller 1012 into a control signal for powerinverter 910 and/or equipment controller 1014. In other embodiments, thebattery power setpoint P_(sp,bat) is provided directly to power inverter910 and used by power inverter 910 to control the battery power P_(bat).

Equipment Controller

Equipment controller 1014 can use the optimal temperature setpointsT_(sp,air) or T_(sp,water) generated by tracking controller 1012 togenerate control signals for powered chiller components 902. The controlsignals generated by equipment controller 1014 may drive the actual(e.g., measured) temperatures T_(air) and/or T_(water) to the setpoints.Equipment controller 1014 can use any of a variety of control techniquesto generate control signals for powered chiller components 902. Forexample, equipment controller 1014 can use state-based algorithms,extremum seeking control (ESC) algorithms, proportional-integral (PI)control algorithms, proportional-integral-derivative (PID) controlalgorithms, model predictive control (MPC) algorithms, or other feedbackcontrol algorithms, to generate control signals for powered chillercomponents 902.

The control signals may include on/off commands, speed setpoints for fan730, power setpoints for compressor 720, pressure setpoints or flow ratesetpoints for pump 732, or other types of setpoints for individualdevices of powered chiller components 902. In other embodiments, thecontrol signals may include the temperature setpoints (e.g., an airtemperature setpoint T_(sp,air), a chilled water temperature setpointT_(sp,water,) etc.) generated by predictive chiller controller 704. Thetemperature setpoints can be provided to powered chiller components 902or local controllers for powered chiller components 902 which operate toachieve the temperature setpoints. For example, a local controller forfan 730 may receive a measurement of the chilled water temperatureT_(water) from chilled water temperature sensor and/or a measurement theair temperature T_(air) from an air temperature sensor and can modulatethe speed of fan 730 to drive the measured temperatures to thetemperature setpoints.

In some embodiments, equipment controller 1014 is configured to providecontrol signals to power inverter 910. The control signals provided topower inverter 910 can include a battery power setpoint P_(sp,bat)and/or the optimal charge/discharge rate Bat_(C/D). Equipment controller1014 can be configured to operate power inverter 910 to achieve thebattery power setpoint P_(sp,bat). For example, equipment controller1014 can cause power inverter 910 to charge battery unit 702 ordischarge battery unit 702 in accordance with the battery power setpointP_(sp,bat).

Pump Unit with Battery and Predictive Control

Referring now to FIGS. 11-12, a pump unit 1100 with a battery unit 1102and predictive pump controller 1104 is shown, according to someembodiments. Pump unit 1100 can be configured to circulate a fluidthrough a HVAC device 1134 via a fluid circuit 1138. HVAC device 1134can include, for example, a heating coil or cooling coil, an airhandling unit, a rooftop unit, a heat exchanger, a refrigerator orfreezer, a condenser or evaporator, a cooling tower, or any other typeof system or device that receives a fluid in a HVAC system. In someembodiments, a pump 1132 receives the fluid (e.g., inlet water 1116) viaan inlet water pipe 1112 and outputs the fluid (e.g., outlet water 1118)via an outlet water pipe 1114.

In some embodiments, battery unit 1102 includes one or more batterycells 1106. Battery cells 1106 are configured to store and dischargeelectric energy (i.e., electricity). In some embodiments, battery unit1102 is charged using electricity from an external energy grid (e.g.,provided by an electric utility). The electricity stored in battery unit1102 can be discharged to power one or more powered components of pumpunit 1100 (e.g., pump 1132). Advantageously, battery unit 1102 allowspump unit 1100 to draw electricity from the energy grid and chargebattery unit 1102 when energy prices are low and discharge the storedelectricity when energy prices are high to time-shift the electric loadof pump unit 1100. In some embodiments, battery unit 1102 has sufficientenergy capacity to power pump unit 1100 for approximately 4-6 hours whenoperating at maximum capacity such that battery unit 1102 can beutilized during high energy cost periods and charged during low energycost periods.

In some embodiments, predictive pump controller 1104 performs anoptimization process to determine whether to charge or discharge batteryunit 1102 during each of a plurality of time steps that occur during anoptimization period. Predictive pump controller 1104 may use weather andpricing data 1110 to predict the amount of heating/cooling required andthe cost of electricity during each of the plurality of time steps.Predictive pump controller 1104 can optimize an objective function thataccounts for the cost of electricity purchased from the energy grid overthe duration of the optimization period. Predictive pump controller 1104can determine an amount of electricity to purchase from the energy gridand an amount of electricity to store or discharge from battery unit1102 during each time step. The objective function and the optimizationperformed by predictive pump controller 1104 are described in greaterdetail with reference to FIGS. 13-14.

Predictive Pump Control System

Referring now to FIG. 13, a block diagram of a predictive pump controlsystem 1300 is shown, according to some embodiments. Several of thecomponents shown in control system 1300 may be part of pump unit 1100.For example, pump unit 1100 may include pump 1132, battery unit 1102,predictive pump controller 1104, power inverter 1310, and a powerjunction 1312.

Power inverter 1310 may be configured to convert electric power betweendirect current (DC) and alternating current (AC). For example, batteryunit 1102 may be configured to store and output DC power, whereas energygrid 1314 and pump 1132 may be configured to consume and provide ACpower. Power inverter 1310 may be used to convert DC power from batteryunit 1102 into a sinusoidal AC output synchronized to the grid frequencyof energy grid 1314 and/or pump 1132. Power inverter 1310 may also beused to convert AC power from energy grid 1314 into DC power that can bestored in battery unit 1102. The power output of battery unit 1102 isshown as P_(bat). P_(bat) may be positive if battery unit 1102 isproviding power to power inverter 1310 (i.e., battery unit 1102 isdischarging) or negative if battery unit 1102 is receiving power frompower inverter 1310 (i.e., battery unit 1102 is charging).

In some instances, power inverter 1310 receives a DC power output frombattery unit 1102 and converts the DC power output to an AC power outputthat can be provided to pump 1132. Power inverter 1310 may synchronizethe frequency of the AC power output with that of energy grid 1314(e.g., 50 Hz or 60 Hz) using a local oscillator and may limit thevoltage of the AC power output to no higher than the grid voltage. Insome embodiments, power inverter 1310 is a resonant inverter thatincludes or uses LC circuits to remove the harmonics from a simplesquare wave in order to achieve a sine wave matching the frequency ofenergy grid 1314. In various embodiments, power inverter 1310 mayoperate using high-frequency transformers, low-frequency transformers,or without transformers. Low-frequency transformers may convert the DCoutput from battery unit 1102 directly to the AC output provided to pump1132. High-frequency transformers may employ a multi-step process thatinvolves converting the DC output to high-frequency AC, then back to DC,and then finally to the AC output provided to pump 1132.

Power junction 1312 is the point at which pump 1132, energy grid 1314,and power inverter 1310 are electrically connected. The power suppliedto power junction 1312 from power inverter 1310 is shown as P_(bat).P_(bat) may be positive if power inverter 1310 is providing power topower junction 1312 (i.e., battery unit 1102 is discharging) or negativeif power inverter 1310 is receiving power from power junction 1312(i.e., battery unit 1102 is charging). The power supplied to powerjunction 1312 from energy grid 1314 is shown as P_(grid). P_(bat) andP_(grid) combine at power junction 1312 to form P_(total) (i.e.,P_(total)=P_(grid)+P_(bat)). P_(total) may be defined as the powerprovided to pump 1132 from power junction 1312. In some instances,P_(total) is greater than P_(grid). For example, when battery unit 1102is discharging, P_(bat) may be positive which adds to the grid powerP_(grid) when P_(bat) combines with P_(grid) to form P_(total). In otherinstances, P_(total) may be less than P_(grid). For example, whenbattery unit 1102 is charging, P_(bat) may be negative which subtractsfrom the grid power P_(grid) when P_(bat) and P_(grid) combine to formP_(total).

Predictive pump controller 1104 can be configured to control pump 1132and power inverter 1310. In some embodiments, predictive pump controller1104 generates and provides a battery power setpoint P_(sp,bat) to powerinverter 1310. The battery power setpoint P_(sp,bat) may include apositive or negative power value (e.g., kW) which causes power inverter1310 to charge battery unit 1102 (when P_(sp,bat) is negative) usingpower available at power junction 1312 or discharge battery unit 1102(when P_(sp,bat) is positive) to provide power to power junction 1312 inorder to achieve the battery power setpoint P_(sp,bat).

In some embodiments, predictive pump controller 1104 generates andprovides control signals to pump 1132. Predictive pump controller 1104may use a multi-stage optimization technique to generate the controlsignals. For example, predictive pump controller 1104 may include aneconomic controller configured to determine the optimal amount of powerto be consumed by pump 1132 at each time step during the optimizationperiod. The optimal amount of power to be consumed may minimize a costfunction that accounts for the cost of energy consumed by pump unit1100. The cost of energy may be based on time-varying energy prices fromelectric utility 1318. In some embodiments, predictive pump controller1104 determines an optimal amount of power to purchase from energy grid1314 (i.e., a grid power setpoint P_(sp,grid)) and an optimal amount ofpower to store or discharge from battery unit 1102 (i.e., a batterypower setpoint P_(sp,bat)) at each of the plurality of time steps.Predictive pump controller 1104 may monitor the actual power usage ofpump 1132 and may utilize the actual power usage as a feedback signalwhen generating the optimal power setpoints.

Predictive pump controller 1104 may include a tracking controllerconfigured to generate flow setpoints Flow_(sp) and differentialpressure setpoints DP_(sp) that achieve the optimal amount of powerconsumption at each time step. In some embodiments, predictive pumpcontroller 1104 uses an equipment model for pump 1132 to determine anamount of fluid flow and/or differential pressure be generated by pump1132 based on the optimal amount of power consumption.

In some embodiments, predictive pump controller 1104 uses the flowsetpoints Flow_(sp) and differential pressure setpoints DP_(sp) togenerate the control signals for pump 1132. The control signals mayinclude on/off commands, speed setpoints, or other types of setpointsthat affect the operation of pump 1132. In other embodiments, thecontrol signals may include the flow setpoints Flow_(sp) anddifferential pressure setpoints DP_(sp) generated by predictive pumpcontroller 1104. The setpoints can be provided to pump 1132 or localcontrollers for pump 1132 which operate to achieve the setpoints. Forexample, a local controller for pump 1132 may receive a measurement ofthe differential pressure DP across pump 1132 from one or more pressuresensors and/or a measurement of the fluid flow caused by pump 1132 fromone or more flow sensors. The local controller can use a feedbackcontrol process (e.g., PID, ESC, MPC, etc.) to increase or decrease thespeed of pump 1132 to drive the measured fluid flow and/or differentialpressure to the setpoint(s). The multi-stage optimization performed bypredictive pump controller 1104 is described in greater detail withreference to FIG. 14.

Predictive Pump Controller

Referring now to FIG. 14, a block diagram illustrating predictive pumpcontroller 1104 in greater detail is shown, according to an exemplaryembodiment. Predictive pump controller 1104 is shown to include acommunications interface 1402 and a processing circuit 1404.Communications interface 1402 may facilitate communications betweencontroller 1104 and external systems or devices. For example,communications interface 1402 may receive measurements of the fluid flowFlow from flow sensors 1416, measurements of the differential pressureDP across pump 1132 from pressure sensors 1418, and measurements of thepower usage of pump 1132. In some embodiments, communications interface1402 receives measurements of the state-of-charge (SOC) of battery unit1102, which can be provided as a percentage of the maximum batterycapacity (i.e., battery %). Communications interface 1402 can receiveweather forecasts from a weather service 916 and predicted energy costsand demand costs from an electric utility 1318. In some embodiments,predictive pump controller 1104 uses communications interface 1402 toprovide control signals pump 1132 and power inverter 1310.

Communications interface 1402 may include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications external systems or devices. In various embodiments, thecommunications may be direct (e.g., local wired or wirelesscommunications) or via a communications network (e.g., a WAN, theInternet, a cellular network, etc.). For example, communicationsinterface 1402 can include an Ethernet card and port for sending andreceiving data via an Ethernet-based communications link or network. Inanother example, communications interface 1402 can include a Wi-Fitransceiver for communicating via a wireless communications network orcellular or mobile phone communications transceivers.

Processing circuit 1404 is shown to include a processor 1406 and memory1408. Processor 1406 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 1406 isconfigured to execute computer code or instructions stored in memory1408 or received from other computer readable media (e.g., CDROM,network storage, a remote server, etc.).

Memory 1408 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 1408 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory1408 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 1408 may be communicably connected toprocessor 1406 via processing circuit 1404 and may include computer codefor executing (e.g., by processor 1406) one or more processes describedherein. When processor 1406 executes instructions stored in memory 1408for completing the various activities described herein, processor 1406generally configures controller 1104 (and more particularly processingcircuit 1404) to complete such activities.

Still referring to FIG. 14, predictive pump controller 1104 is shown toinclude an economic controller 1410, a tracking controller 1412, and anequipment controller 1414. Controllers 1410-1414 can be configured toperform a multi-state optimization process to generate control signalsfor power inverter 1310 and pump 1132. In brief overview, economiccontroller 1410 can optimize a predictive cost function to determine anoptimal amount of power to purchase from energy grid 1314 (i.e., a gridpower setpoint P_(sp,grid)), an optimal amount of power to store ordischarge from battery unit 1102 (i.e., a battery power setpointP_(sp,bat)), and/or an optimal amount of power to be consumed by pump1132 (i.e., a pump power setpoint P_(sp,pump)) at each time step of anoptimization period. Tracking controller 1412 can use the optimal powersetpoints P_(sp,grid), P_(sp,bat), and/or P_(sp,pump) to determineoptimal flow setpoints Flow_(sp), pressure setpoints DP_(sp), and anoptimal battery charge or discharge rate (i.e., Bat_(C/D)). Equipmentcontroller 1414 can use the optimal setpoints Flow_(sp) and/or DP_(sp)to generate control signals for pump 1132 that drive the actual (e.g.,measured) flowrate Flow and/or pressure DP to the setpoints (e.g., usinga feedback control technique). Each of controllers 1410-1414 isdescribed in detail below.

Economic Controller

Economic controller 1410 can be configured to optimize a predictive costfunction to determine an optimal amount of power to purchase from energygrid 1314 (i.e., a grid power setpoint P_(sp,grid)), an optimal amountof power to store or discharge from battery unit 1102 (i.e., a batterypower setpoint P_(sp,bat)), and/or an optimal amount of power to beconsumed by pump 1132 (i.e., a pump power setpoint P_(sp,pump)) at eachtime step of an optimization period. An example of a predictive costfunction which can be optimized by economic controller 1410 is shown inthe following equation:

${\min (J)} = {{\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{pump}(k)}\Delta \; t}} + {C_{DC}{\max\limits_{k}\left( {P_{grid}(k)} \right)}} - {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{bat}(k)}\Delta \; t}}}$

where C_(ec)(k) is the cost per unit of electricity (e.g., $/kWh)purchased from electric utility 1318 during time step k, P_(pump)(k) isthe power consumption of pump 1132 at time step k, C_(DC) is the demandcharge rate (e.g., $/kW), where the max( ) term selects the maximumelectricity purchase of pump unit 1100 (i.e., the maximum value ofP_(grid)(k)) during any time step k of the optimization period,P_(bat)(k) is the amount of power discharged from battery unit 1102during time step k, and Δt is the duration of each time step k. Economiccontroller 1410 can optimize the predictive cost function J over theduration of the optimization period (e.g., from time step k=1 to timestep k=h) to predict the total cost of operating pump unit 1100 over theduration of the optimization period.

The first term of the predictive cost function J represents the cost ofelectricity consumed by pump 1132 over the duration of the optimizationperiod. The values of the parameter C_(ec)(k) at each time step k can bedefined by the energy cost information provided by electric utility1318. In some embodiments, the cost of electricity varies as a functionof time, which results in different values of C_(ec)(k) at differenttime steps k. The variable P_(pump)(k) is a decision variable which canbe optimized by economic controller 1410.

The second term of the predictive cost function J represents the demandcharge. Demand charge is an additional charge imposed by some utilityproviders based on the maximum power consumption during an applicabledemand charge period. For example, the demand charge rate C_(DC) may bespecified in terms of dollars per unit of power (e.g., $/kW) and may bemultiplied by the peak power usage (e.g., kW) during a demand chargeperiod to calculate the demand charge. In the predictive cost functionJ, the demand charge rate C_(DC) may be defined by the demand costinformation received from electric utility 1318. The variableP_(grid)(k) is a decision variable which can be optimized by economiccontroller 1410 in order to reduce the peak power usage max(P_(grid)(k))that occurs during the demand charge period. Load shifting may alloweconomic controller 1410 to smooth momentary spikes in the electricdemand of pump unit 1100 by storing energy in battery unit 1102 when thepower consumption of pump 1132 is low. The stored energy can bedischarged from battery unit 1102 when the power consumption of pump1132 is high in order to reduce the peak power draw P_(grid) from energygrid 1314, thereby decreasing the demand charge incurred.

The final term of the predictive cost function J represents the costsavings resulting from the use of battery unit 1102. Unlike the previousterms in the cost function J, the final term subtracts from the totalcost. The values of the parameter C_(ec)(k) at each time step k can bedefined by the energy cost information provided by electric utility1318. In some embodiments, the cost of electricity varies as a functionof time, which results in different values of C_(ec)(k) at differenttime steps k. The variable P_(bat)(k) is a decision variable which canbe optimized by economic controller 1410. A positive value of P_(bat)(k)indicates that battery unit 1102 is discharging, whereas a negativevalue of P_(bat)(k) indicates that battery unit 1102 is charging. Thepower discharged from battery unit 1102 P_(bat)(k) can be used tosatisfy some or all of the total power consumption P_(total)(k) of pump1132, which reduces the amount of power P_(grid)(k) purchased fromenergy grid 1314 (i.e., P_(grid)(k)=P_(total)(k)−P_(bat)(k)). However,charging battery unit 1102 results in a negative value of P_(bat)(k)which adds to the total amount of power P_(grid)(k) purchased fromenergy grid 1314.

Economic controller 1410 can optimize the predictive cost function Jover the duration of the optimization period to determine optimal valuesof the decision variables at each time step during the optimizationperiod. In some embodiments, the optimization period has a duration ofapproximately one day and each time step is approximately fifteenminutes. However, the durations of the optimization period and the timesteps can vary in other embodiments and can be adjusted by a user.Advantageously, economic controller 1410 can use battery unit 1102 toperform load shifting by drawing electricity from energy grid 1314 whenenergy prices are low and/or when the power consumed by pump 1132 islow. The electricity can be stored in battery unit 1102 and dischargedlater when energy prices are high and/or the power consumption of pump1132 is high. This enables economic controller 1410 to reduce the costof electricity consumed by pump unit 1100 and can smooth momentaryspikes in the electric demand of pump unit 1100, thereby reducing thedemand charge incurred.

Economic controller 1410 can be configured to impose constraints on theoptimization of the predictive cost function J. In some embodiments, theconstraints include constraints on the flow rate Flow and/ordifferential pressure DP produced by pump 1132. Economic controller 1410can be configured to maintain the actual or predicted flow rate Flowbetween a minimum flow bound Flow_(min) and a maximum flow boundFlow_(max) (i.e., Flow_(min)≤Flow≤Flow_(max)) at all times. Theparameters Flow_(min) and Flow_(max) may be time-varying to definedifferent flow ranges at different times. Similarly, economic controller1410 can be configured to maintain the actual or predicted pressure DPbetween a minimum pressure bound DP_(min) and a maximum pressure boundDP_(max) (i.e., DP_(min)≤DP≤DP_(max)) at all times. The parametersDP_(min) and DP_(max) may be time-varying to define different flowranges at different times.

In addition to constraints on the fluid flowrate Flow and thedifferential pressure DP, economic controller 1410 can imposeconstraints on the state-of-charge (SOC) and charge/discharge rates ofbattery unit 1102. In some embodiments, economic controller 1410generates and imposes the following power constraints on the predictivecost function J:

P _(bat) ≤P _(rated) −P _(bat) ≤P _(rated)

where P_(bat) is the amount of power discharged from battery unit 1102and P_(rated) is the rated battery power of battery unit 1102 (e.g., themaximum rate at which battery unit 1102 can be charged or discharged).These power constraints ensure that battery unit 1102 is not charged ordischarged at a rate that exceeds the maximum possible batterycharge/discharge rate P_(rated).

In some embodiments, economic controller 1410 generates and imposes oneor more capacity constraints on the predictive cost function J Thecapacity constraints may be used to relate the battery power P_(bat)charged or discharged during each time step to the capacity and SOC ofbattery unit 1102. The capacity constraints may ensure that the capacityof battery unit 1102 is maintained within acceptable lower and upperbounds at each time step of the optimization period. In someembodiments, economic controller 1410 generates the following capacityconstraints:

C _(a)(k)−P _(bat)(k)Δt≤C _(rated)

C _(a)(k)−P _(bat)(k)Δt≥0

where C_(a)(k) is the available battery capacity (e.g., kWh) at thebeginning of time step k, P_(bat)(k) is the rate at which battery unit1102 is discharged during time step k (e.g., kW), Δt is the duration ofeach time step, and C_(rated) is the maximum rated capacity of batteryunit 1102 (e.g., kWh). The term P_(bat)(k)Δt represents the change inbattery capacity during time step k. These capacity constraints ensurethat the capacity of battery unit 1102 is maintained between zero andthe maximum rated capacity C_(rated).

In some embodiments, economic controller 1410 generates and imposes oneor more capacity constraints on the operation of pump 1132. For example,pump 1132 may have a maximum operating point (e.g., a maximum pumpspeed, a maximum differential pressure, etc.) which corresponds to amaximum power consumption P_(pump,max). Economic controller 1410 can beconfigured to generate a constraint which limits the power P_(pump)provided to pump 1132 between zero and the maximum power consumptionP_(pump,max) as shown in the following equation:

0≤P _(pump) ≤P _(pump,max)

P _(pump) =P _(sp,grid) +P _(sp,bat)

where the total power P_(pump) provided to pump 1132 is the sum of thegrid power setpoint P_(sp,grid) and the battery power setpointP_(sp,bat).

Economic controller 1410 can optimize the predictive cost function Jsubject to the constraints to determine optimal values for the decisionvariables P_(pump), P_(grid), and P_(bat), whereP_(pump)=P_(bat)+P_(grid). In some embodiments, economic controller 1410uses the optimal values for P_(pump), P_(bat), and/or P_(grid) togenerate power setpoints for tracking controller 1412. The powersetpoints can include battery power setpoints P_(sp,bat), grid powersetpoints P_(sp,grid), and/or pump power setpoints P_(sp,pump) for eachof the time steps k in the optimization period. Economic controller 1410can provide the power setpoints to tracking controller 1412.

Tracking Controller

Tracking controller 1412 can use the optimal power setpointsP_(sp,grid), P_(sp,bat), and/or P_(sp,pump) generated by economiccontroller 1410 to determine optimal flow setpoints Flow_(sp), optimalpressure setpoints DP_(sp), and an optimal battery charge or dischargerate (i.e., Bat_(C/D)). In some embodiments, tracking controller 1412generates a flow setpoint Flow_(sp) and/or a pressure setpoint DP_(sp)that are predicted to achieve the power setpoint P_(sp,pump) for pump1132. In other words, tracking controller 1412 may generate a flowsetpoint Flow_(sp) and/or a pressure setpoint DP_(sp) that cause pump1132 to consume the optimal amount of power P_(pump) determined byeconomic controller 1410.

In some embodiments, tracking controller 1412 uses the battery powersetpoint P_(sp,bat) to determine the optimal rate Bat_(C/D) at which tocharge or discharge battery unit 1102. For example, the battery powersetpoint P_(sp,bat) may define a power value (kW) which can betranslated by tracking controller 1412 into a control signal for powerinverter 1310 and/or equipment controller 1414. In other embodiments,the battery power setpoint P_(sp,bat) is provided directly to powerinverter 1310 and used by power inverter 1310 to control the batterypower P_(bat).

Equipment Controller

Equipment controller 1414 can use the optimal flow setpoints Flow_(sp)and/or a pressure setpoints DP_(sp) generated by tracking controller1412 to generate control signals for pump 1132. The control signalsgenerated by equipment controller 1414 may drive the actual (e.g.,measured) flow rate Flow and pressure DP to the setpoints. Equipmentcontroller 1414 can use any of a variety of control techniques togenerate control signals for pump 1132. For example, equipmentcontroller 1414 can use state-based algorithms, extremum seeking control(ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, or other feedback controlalgorithms, to generate control signals for pump 1132.

The control signals may include on/off commands, speed commands for pump1132, power commands for pump 1132, or other types of operating commandsfor pump 1132. In other embodiments, the control signals may include theflow setpoints Flow_(sp) and/or a pressure setpoints DP_(sp) generatedby predictive pump controller 1104. The setpoints can be provided topump 1132 or a local controller for pump 1132 which operate to achievethe setpoints. For example, a local controller for pump 1132 may receivea measurement of the fluid flowrate Flow from flow sensors 1416 and/or ameasurement the differential pressure DP from pressure sensors 1418 andcan modulate the speed of pump 1132 to drive the measured flowrateand/or pressure to the setpoints.

In some embodiments, equipment controller 1414 is configured to providecontrol signals to power inverter 1310. The control signals provided topower inverter 1310 can include a battery power setpoint P_(sp,bat)and/or the optimal charge/discharge rate Bat_(C/D). Equipment controller1414 can be configured to operate power inverter 1310 to achieve thebattery power setpoint P_(sp,bat). For example, equipment controller1414 can cause power inverter 1310 to charge battery unit 1102 ordischarge battery unit 1102 in accordance with the battery powersetpoint P_(sp,bat).

Cooling Tower with Battery Unit and Predictive Control

Referring now to FIG. 15, a cooling tower system 1500 is shown,according to some embodiments. System 1500 is shown to include a coolingtower 1512 and a battery unit 1502 with a predictive cooling towercontroller 1504. Cooling tower 1512 can be configured to provide coolingto a cooling load 1522. Cooling load 1522 can include, for example, abuilding zone, a supply airstream flowing through an air duct, anairflow in an air handling unit or rooftop unit, fluid flowing through aheat exchanger, a refrigerator or freezer, a condenser or evaporator, acooling coil, or any other type of system, device, or space whichrequires cooling. In some embodiments, a pump 1516 circulates a chilledfluid to cooling load 1522 via a cooling tower circuit 1532. The chilledfluid can absorb heat from cooling load 1522, thereby providing coolingto cooling load 1522 and warming the chilled fluid.

Cooling tower 1512 can be configured to cool the water in cooling towercircuit 1532 by transferring heat from the water to outside air. Coolingtower 1512 may include a fan 1514 which causes cool air to flow throughcooling tower 1512. Cooling tower 1512 places the cool air in a heatexchange relationship with the warmer water, thereby transferring heatfrom warmer water to the cooler air. Although cooling tower circuit 1532is shown and described as circulating water, it should be understoodthat any type of coolant or working fluid (e.g., water, glycol, CO₂,etc.) can be used in cooling tower circuit 1532.

Still referring to FIG. 15, system 1500 is shown to include a batteryunit 1502. In some embodiments, battery unit 1502 includes one or morephotovoltaic (PV) panels 1508. PV panels 1508 may include a collectionof photovoltaic cells. The photovoltaic cells are configured to convertsolar energy (i.e., sunlight) into electricity using a photovoltaicmaterial such as monocrystalline silicon, polycrystalline silicon,amorphous silicon, cadmium telluride, copper indium galliumselenide/sulfide, or other materials that exhibit the photovoltaiceffect. In some embodiments, the photovoltaic cells are contained withinpackaged assemblies that form PV panels 1508. Each PV panel 1508 mayinclude a plurality of linked photovoltaic cells. PV panels 1508 maycombine to form a photovoltaic array.

In some embodiments, PV panels 1508 are configured to maximize solarenergy collection. For example, battery unit 1502 may include a solartracker (e.g., a GPS tracker, a sunlight sensor, etc.) that adjusts theangle of PV panels 1508 so that PV panels 1508 are aimed directly at thesun throughout the day. The solar tracker may allow PV panels 1508 toreceive direct sunlight for a greater portion of the day and mayincrease the total amount of power produced by PV panels 1508. In someembodiments, battery unit 1502 includes a collection of mirrors, lenses,or solar concentrators configured to direct and/or concentrate sunlighton PV panels 1508. The energy generated by PV panels 1508 may be storedin battery cells 1506 and/or used to power various components of coolingtower 1512.

In some embodiments, battery unit 1502 includes one or more batterycells 1506. Battery cells 1506 are configured to store and dischargeelectric energy (i.e., electricity). In some embodiments, battery unit1502 is charged using electricity from an external energy grid (e.g.,provided by an electric utility). The electricity stored in battery unit1502 can be discharged to power one or more powered components ofcooling tower 1512 (e.g., fan 1514, pump 1516, etc.). Advantageously,battery unit 1502 allows cooling tower 1512 to draw electricity from theenergy grid and charge battery unit 1502 when energy prices are low anddischarge the stored electricity when energy prices are high totime-shift the electric load of cooling tower 1512. In some embodiments,battery unit 1502 has sufficient energy capacity to power cooling tower1512 for approximately 4-6 hours when operating at maximum capacity suchthat battery unit 1502 can be utilized during high energy cost periodsand charged during low energy cost periods.

In some embodiments, predictive cooling tower controller 1504 performsan optimization process to determine whether to charge or dischargebattery unit 1502 during each of a plurality of time steps that occurduring an optimization period. Predictive cooling tower controller 1504may use weather and pricing data 1510 to predict the amount ofheating/cooling required and the cost of electricity during each of theplurality of time steps. Predictive cooling tower controller 1504 canoptimize an objective function that accounts for the cost of electricitypurchased from the energy grid over the duration of the optimizationperiod. In some embodiments, the objective function also accounts forthe cost of operating various components of cooling tower 1512 (e.g.,cost of natural gas used to fuel boilers). Predictive cooling towercontroller 1504 can determine an amount of electricity to purchase fromthe energy grid and an amount of electricity to store or discharge frombattery unit 1502 during each time step. The objective function and theoptimization performed by predictive cooling tower controller 1504 aredescribed in greater detail with reference to FIGS. 16-17.

Predictive Cooling Tower Control System

Referring now to FIG. 16, a block diagram of a predictive cooling towercontrol system 1600 is shown, according to some embodiments. Several ofthe components shown in control system 1600 may be part of cooling tower1512. For example, cooling tower 1512 may include powered cooling towercomponents 1602, battery unit 1502, predictive cooling tower controller1504, power inverter 1610, and a power junction 1612. Powered coolingtower components 1602 may include any component of cooling tower 1512that consumes power (e.g., electricity) during operation. For example,powered cooling tower components 1602 are shown to include cooling fan1514 and pump 1516.

Power inverter 1610 may be configured to convert electric power betweendirect current (DC) and alternating current (AC). For example, batteryunit 1502 may be configured to store and output DC power, whereas energygrid 1614 and powered cooling tower components 1602 may be configured toconsume and provide AC power. Power inverter 1610 may be used to convertDC power from battery unit 1502 into a sinusoidal AC output synchronizedto the grid frequency of energy grid 1614 and/or powered cooling towercomponents 1602. Power inverter 1610 may also be used to convert ACpower from energy grid 1614 into DC power that can be stored in batteryunit 1502. The power output of battery unit 1502 is shown as P_(bat).P_(bat) may be positive if battery unit 1502 is providing power to powerinverter 1610 (i.e., battery unit 1502 is discharging) or negative ifbattery unit 1502 is receiving power from power inverter 1610 (i.e.,battery unit 1502 is charging).

In some instances, power inverter 1610 receives a DC power output frombattery unit 1502 and converts the DC power output to an AC power outputthat can be provided to powered cooling tower components 1602. Powerinverter 1610 may synchronize the frequency of the AC power output withthat of energy grid 1614 (e.g., 50 Hz or 60 Hz) using a local oscillatorand may limit the voltage of the AC power output to no higher than thegrid voltage. In some embodiments, power inverter 1610 is a resonantinverter that includes or uses LC circuits to remove the harmonics froma simple square wave in order to achieve a sine wave matching thefrequency of energy grid 1614. In various embodiments, power inverter1610 may operate using high-frequency transformers, low-frequencytransformers, or without transformers. Low-frequency transformers mayconvert the DC output from battery unit 1502 directly to the AC outputprovided to powered cooling tower components 1602. High-frequencytransformers may employ a multi-step process that involves convertingthe DC output to high-frequency AC, then back to DC, and then finally tothe AC output provided to powered cooling tower components 1602.

The power output of PV panels 1508 is shown as P_(PV). The power outputP_(PV) of PV panels 1508 can be stored in battery unit 1502 and/or usedto power powered cooling tower components 1602. In some embodiments, PVpanels 1508 measure the amount of power P_(PV) generated by PV panels1508 and provides an indication of the PV power to predictive coolingtower controller 1504. For example, PV panels 1508 are shown providingan indication of the PV power percentage (i.e., PV %) to predictivecooling tower controller 1504. The PV power percentage may represent apercentage of the maximum PV power at which PV panels 1508 are currentlyoperating.

Power junction 1612 is the point at which powered cooling towercomponents 1602, energy grid 1614, PV panels 1508, and power inverter1610 are electrically connected. The power supplied to power junction1612 from power inverter 1610 is shown as P_(bat). P_(bat) may bepositive if power inverter 1610 is providing power to power junction1612 (i.e., battery unit 1502 is discharging) or negative if powerinverter 1610 is receiving power from power junction 1612 (i.e., batteryunit 1502 is charging). The power supplied to power junction 1612 fromenergy grid 1614 is shown as P_(grid) and the power supplied to powerjunction 1612 from PV panels 1508 is shown as P_(PV). P_(bat,) P_(PV),and P_(grid) combine at power junction 1612 to form P_(total) (i.e.,P_(total)=P_(grid)+P_(bat)+P_(PV)). P_(total) may be defined as thepower provided to powered cooling tower components 1602 from powerjunction 1612. In some instances, P_(total) is greater than P_(grid).For example, when battery unit 1502 is discharging, P_(bat) may bepositive which adds to the grid power P_(grid) and the PV power P_(PV)when P_(bat) and P_(PV) combine with P_(grid) to form P_(total). Inother instances, P_(total) may be less than P_(grid). For example, whenbattery unit 1502 is charging, P_(bat) may be negative which subtractsfrom the grid power P_(grid) and the PV power P_(PV) when P_(bat),P_(PV), and P_(grid) combine to form P_(total).

Predictive cooling tower controller 1504 can be configured to controlpowered cooling tower components 1602 and power inverter 1610. In someembodiments, predictive cooling tower controller 1504 generates andprovides a battery power setpoint P_(sp,bat) to power inverter 1610. Thebattery power setpoint P_(sp,bat) may include a positive or negativepower value (e.g., kW) which causes power inverter 1610 to chargebattery unit 1502 (when P_(sp,bat) is negative) using power available atpower junction 1612 or discharge battery unit 1502 (when P_(sp,bat) ispositive) to provide power to power junction 1612 in order to achievethe battery power setpoint P_(sp,bat).

In some embodiments, predictive cooling tower controller 1504 generatesand provides control signals to powered cooling tower components 1602.Predictive cooling tower controller 1504 may use a multi-stageoptimization technique to generate the control signals. For example,predictive cooling tower controller 1504 may include an economiccontroller configured to determine the optimal amount of power to beconsumed by powered cooling tower components 1602 at each time stepduring the optimization period. The optimal amount of power to beconsumed may minimize a cost function that accounts for the cost ofenergy consumed by cooling tower 1512. The cost of energy may be basedon time-varying energy prices from electric utility 1618. In someembodiments, predictive cooling tower controller 1504 determines anoptimal amount of power to purchase from energy grid 1614 (i.e., a gridpower setpoint P_(sp,grid)) and an optimal amount of power to store ordischarge from battery unit 1502 (i.e., a battery power setpointP_(sp,bat)) at each of the plurality of time steps. Predictive coolingtower controller 1504 may monitor the actual power usage of poweredcooling tower components 1602 and may utilize the actual power usage asa feedback signal when generating the optimal power setpoints.

Predictive cooling tower controller 1504 may include a trackingcontroller configured to generate temperature setpoints that achieve theoptimal amount of power consumption at each time step. The temperaturesetpoints may include, for example, a sump water temperature setpointT_(sp,sump) (i.e., a temperature setpoint for the water in sump 1518)and/or a condenser water temperature setpoint T_(sp,cond) (i.e., atemperature setpoint for the warm water returning to cooling tower1512). In some embodiments, predictive cooling tower controller 1504uses equipment models for powered cooling tower components 1602 todetermine an amount of cooling that can be generated by cooling tower1512 based on the optimal amount of power consumption.

In some embodiments, predictive cooling tower controller 1504 uses thetemperature setpoints to generate the control signals for poweredcooling tower components 1602. The control signals may include on/offcommands, speed setpoints for fan 1514, differential pressure setpointsor flow rate setpoints for pump 1516, or other types of setpoints forindividual devices of powered cooling tower components 1602. In otherembodiments, the control signals may include the temperature setpoints(e.g., a sump water temperature setpoint T_(sp,sump), a condenser watertemperature setpoint T_(sp,cond), etc.) generated by predictive coolingtower controller 1504. The temperature setpoints can be provided topowered cooling tower components 1602 or local controllers for poweredcooling tower components 1602 which operate to achieve the temperaturesetpoints. For example, a local controller for fan 1514 may receive ameasurement of the sump water temperature T_(cump) from a sump watertemperature sensor and/or a measurement the condenser temperatureT_(cond) from a condenser water temperature sensor. The local controllercan use a feedback control process (e.g., PID, ESC, MPC, etc.) toincrease or decrease the speed of fan 1514 to drive the measuredtemperature(s) to the temperature setpoint(s). Similar feedback controlprocesses can be used to control pump 1516. The multi-stage optimizationperformed by predictive cooling tower controller 1504 is described ingreater detail with reference to FIG. 17.

Predictive Cooling Tower Controller

Referring now to FIG. 17, a block diagram illustrating predictivecooling tower controller 1504 in greater detail is shown, according toan exemplary embodiment. Predictive cooling tower controller 1504 isshown to include a communications interface 1702 and a processingcircuit 1704. Communications interface 1702 may facilitatecommunications between controller 1504 and external systems or devices.For example, communications interface 1702 may receive measurements ofthe sump water temperature T_(sump) and the condenser water temperatureT_(cond) from temperature sensors 1716 and measurements of the powerusage of powered cooling tower components 1602. In some embodiments,communications interface 1702 receives measurements of thestate-of-charge (SOC) of battery unit 1502, which can be provided as apercentage of the maximum battery capacity (i.e., battery %).Communications interface 1702 can receive weather forecasts from aweather service 1616 and predicted energy costs and demand costs from anelectric utility 1618. In some embodiments, predictive cooling towercontroller 1504 uses communications interface 1702 to provide controlsignals powered cooling tower components 1602 and power inverter 1610.

Communications interface 1702 may include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications external systems or devices. In various embodiments, thecommunications may be direct (e.g., local wired or wirelesscommunications) or via a communications network (e.g., a WAN, theInternet, a cellular network, etc.). For example, communicationsinterface 1702 can include an Ethernet card and port for sending andreceiving data via an Ethernet-based communications link or network. Inanother example, communications interface 1702 can include a Wi-Fitransceiver for communicating via a wireless communications network orcellular or mobile phone communications transceivers.

Processing circuit 1704 is shown to include a processor 1706 and memory1708. Processor 1706 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 1706 isconfigured to execute computer code or instructions stored in memory1708 or received from other computer readable media (e.g., CDROM,network storage, a remote server, etc.).

Memory 1708 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 1708 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory1708 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 1708 may be communicably connected toprocessor 1706 via processing circuit 1704 and may include computer codefor executing (e.g., by processor 1706) one or more processes describedherein. When processor 1706 executes instructions stored in memory 1708for completing the various activities described herein, processor 1706generally configures controller 1504 (and more particularly processingcircuit 1704) to complete such activities.

Still referring to FIG. 17, predictive cooling tower controller 1504 isshown to include an economic controller 1710, a tracking controller1712, and an equipment controller 1714. Controllers 1710-1714 can beconfigured to perform a multi-state optimization process to generatecontrol signals for power inverter 1610 and powered cooling towercomponents 1602. In brief overview, economic controller 1710 canoptimize a predictive cost function to determine an optimal amount ofpower to purchase from energy grid 1614 (i.e., a grid power setpointP_(sp,grid)), an optimal amount of power to store or discharge frombattery unit 1502 (i.e., a battery power setpoint P_(sp,bat)), and/or anoptimal amount of power to be consumed by powered cooling towercomponents 1602 (i.e., a cooling tower power setpoint P_(sp,total)) ateach time step of an optimization period. Tracking controller 1712 canuse the optimal power setpoints P_(sp,grid), P_(sp,bat), and/orP_(sp,total) to determine optimal temperature setpoints (e.g., a sumpwater temperature setpoint T_(sp,sump), a condenser water temperaturesetpoint T_(sp,cond), etc.) and an optimal battery charge or dischargerate (i.e., Bat_(C/D)). Equipment controller 1714 can use the optimaltemperature setpoints T_(sp,zone) or T_(sp,chw) to generate controlsignals for powered cooling tower components 1602 that drive the actual(e.g., measured) temperatures T_(zone) and/or T_(chw) to the setpoints(e.g., using a feedback control technique). Each of controllers1710-1714 is described in detail below.

Economic Controller

Economic controller 1710 can be configured to optimize a predictive costfunction to determine an optimal amount of power to purchase from energygrid 1614 (i.e., a grid power setpoint P_(sp,grid)), an optimal amountof power to store or discharge from battery unit 1502 (i.e., a batterypower setpoint P_(sp,bat)), and/or an optimal amount of power to beconsumed by powered cooling tower components 1602 (i.e., a cooling towerpower setpoint P_(sp,total)) at each time step of an optimizationperiod. An example of a predictive cost function which can be optimizedby economic controller 1710 is shown in the following equation:

${\min (J)} = {{\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{fan}(k)}\Delta \; t}} + {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{pump}(k)}\Delta \; t}} + {C_{DC}{\max\limits_{k}\left( {P_{grid}(k)} \right)}} - {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{bat}(k)}\Delta \; t}}}$

where C_(ec)(k) is the cost per unit of electricity (e.g., $/kWh)purchased from electric utility 1618 during time step k, P_(fan)(k) isthe power consumption (e.g., kW) of fan 1514 during time step k,P_(pump)(k) is the power consumption of pump 1516 at time step k, C_(DC)is the demand charge rate (e.g., $/kW), where the max( ) term selectsthe maximum electricity purchase of cooling tower 1512 (i.e., themaximum value of P_(grid)(k)) during any time step k of the optimizationperiod, P_(bat)(k) is the amount of power discharged from battery unit1502 during time step k, and Δt is the duration of each time step k.Economic controller 1710 can optimize the predictive cost function Jover the duration of the optimization period (e.g., from time step k=1to time step k=h) to predict the total cost of operating cooling tower1512 over the duration of the optimization period.

The first and second terms of the predictive cost function J representthe cost of electricity consumed by powered cooling tower components1602 over the duration of the optimization period. The values of theparameter C_(ec)(k) at each time step k can be defined by the energycost information provided by electric utility 1618. In some embodiments,the cost of electricity varies as a function of time, which results indifferent values of C_(ec)(k) at different time steps k. The variablesP_(fan)(k) and P_(cond)(k) are decision variables which can be optimizedby economic controller 1710. In some embodiments, the total powerconsumption P_(total)(k) of powered cooling tower components 1602 attime step k is equal to the sum of P_(fan)(k) and P_(pump)(k) (i.e.,P_(total)(k)=P_(fan)(k)+P_(pump)(k)). Accordingly, the first two termsof the predictive cost function can be replaced with the summationΣ_(k=1) ^(h)C_(ec)(k)P_(total)(k)Δt in some embodiments.

The third term of the predictive cost function J represents the demandcharge. Demand charge is an additional charge imposed by some utilityproviders based on the maximum power consumption during an applicabledemand charge period. For example, the demand charge rate C_(DC) may bespecified in terms of dollars per unit of power (e.g., $/kW) and may bemultiplied by the peak power usage (e.g., kW) during a demand chargeperiod to calculate the demand charge. In the predictive cost functionJ, the demand charge rate C_(DC) may be defined by the demand costinformation received from electric utility 1618. The variableP_(grid)(k) is a decision variable which can be optimized by economiccontroller 1710 in order to reduce the peak power usage max(P_(grid)(k))that occurs during the demand charge period. Load shifting may alloweconomic controller 1710 to smooth momentary spikes in the electricdemand of cooling tower 1512 by storing energy in battery unit 1502 whenthe power consumption of powered cooling tower components 1602 is low.The stored energy can be discharged from battery unit 1502 when thepower consumption of powered cooling tower components 1602 is high inorder to reduce the peak power draw P_(grid) from energy grid 1614,thereby decreasing the demand charge incurred.

The final term of the predictive cost function J represents the costsavings resulting from the use of battery unit 1502. Unlike the previousterms in the cost function J, the final term subtracts from the totalcost. The values of the parameter C_(ec)(k) at each time step k can bedefined by the energy cost information provided by electric utility1618. In some embodiments, the cost of electricity varies as a functionof time, which results in different values of C_(ec)(k) at differenttime steps k. The variable P_(bat)(k) is a decision variable which canbe optimized by economic controller 1710. A positive value of P_(bat)(k)indicates that battery unit 1502 is discharging, whereas a negativevalue of P_(bat)(k) indicates that battery unit 1502 is charging. Thepower discharged from battery unit 1502 P_(bat)(k) can be used tosatisfy some or all of the total power consumption P_(total)(k) ofpowered cooling tower components 1602, which reduces the amount of powerP_(grid)(k) purchased from energy grid 1614 (i.e.,P_(grid)(k)=P_(total)(k)−P_(bat)(k)−P_(PV)(k)). However, chargingbattery unit 1502 results in a negative value of P_(bat)(k) which addsto the total amount of power P_(grid)(k) purchased from energy grid1614.

In some embodiments, the power P_(PV) provided by PV panels 1508 is notincluded in the predictive cost function J because generating PV powerdoes not incur a cost. However, the power P_(PV) generated by PV panels1508 can be used to satisfy some or all of the total power consumptionP_(total)(k) of powered cooling tower components 1602, which reduces theamount of power P_(grid)(k) purchased from energy grid 1614 (i.e.,P_(grid)(k)=P_(total)(k)−P_(bat)(k)−P_(PV)(k)). The amount of PV powerP_(PV) generated during any time step k can be predicted by economiccontroller 1710. Several techniques for predicting the amount of PVpower generated by PV panels are described in U.S. patent applicationSer. No. 15/247,869, U.S. patent application Ser. No. 15/247,844, andU.S. patent application Ser. No. 15/247,788. Each of these patentapplications has a filing date of Aug. 25, 2016, and the entiredisclosure of each of these patent applications is incorporated byreference herein.

Economic controller 1710 can optimize the predictive cost function Jover the duration of the optimization period to determine optimal valuesof the decision variables at each time step during the optimizationperiod. In some embodiments, the optimization period has a duration ofapproximately one day and each time step is approximately fifteenminutes. However, the durations of the optimization period and the timesteps can vary in other embodiments and can be adjusted by a user.Advantageously, economic controller 1710 can use battery unit 1502 toperform load shifting by drawing electricity from energy grid 1614 whenenergy prices are low and/or when the power consumed by powered coolingtower components 1602 is low. The electricity can be stored in batteryunit 1502 and discharged later when energy prices are high and/or thepower consumption of powered cooling tower components 1602 is high. Thisenables economic controller 1710 to reduce the cost of electricityconsumed by cooling tower 1512 and can smooth momentary spikes in theelectric demand of cooling tower 1512, thereby reducing the demandcharge incurred.

Economic controller 1710 can be configured to impose constraints on theoptimization of the predictive cost function J. In some embodiments, theconstraints include constraints on the temperature T_(sump) of the sumpwater produced by cooling tower 1512. Economic controller 1710 can beconfigured to maintain the actual or predicted temperature T_(zump)between a minimum temperature bound T_(min) and a maximum temperaturebound T_(max) (i.e., T_(min)≤T_(sump)≤T_(max)) at all times. Similarly,economic controller 1710 can be configured to maintain the actual orpredicted temperature T_(cond) between a minimum temperature boundT_(min) and a maximum temperature bound T_(max) (i.e.,T_(min)≤T_(cond)≤T_(max)) at all times. The parameters T_(min) andT_(max) may be time-varying to define different temperature ranges atdifferent times.

In order to ensure that the temperature constraints are satisfied,economic controller 1710 can model the temperatures T_(sump) andT_(cond) as a function of the decision variables optimized by economiccontroller 1710. Several techniques for developing temperature modelsand relating temperatures to the decision variables in the predictivecost function J are described in greater detail in U.S. Pat. No.9,436,179 granted Sep. 6, 2016, U.S. patent application Ser. No.14/694,633 filed Apr. 23, 2015, and U.S. patent application Ser. No.15/199,910 filed Jun. 30, 2016. The entire disclosure of each of thesepatents and patent applications is incorporated by reference herein.

In addition to constraints on the temperature T_(sump) and T_(cond),economic controller 1710 can impose constraints on the state-of-charge(SOC) and charge/discharge rates of battery unit 1502. In someembodiments, economic controller 1710 generates and imposes thefollowing power constraints on the predictive cost function J:

P _(bat) ≤P _(rated) −P _(bat) ≤P _(rated)

where P_(bat) is the amount of power discharged from battery unit 1502and P_(rated) is the rated battery power of battery unit 1502 (e.g., themaximum rate at which battery unit 1502 can be charged or discharged).These power constraints ensure that battery unit 1502 is not charged ordischarged at a rate that exceeds the maximum possible batterycharge/discharge rate P_(rated).

In some embodiments, economic controller 1710 generates and imposes oneor more capacity constraints on the predictive cost function J Thecapacity constraints may be used to relate the battery power P_(bat)charged or discharged during each time step to the capacity and SOC ofbattery unit 1502. The capacity constraints may ensure that the capacityof battery unit 1502 is maintained within acceptable lower and upperbounds at each time step of the optimization period. In someembodiments, economic controller 1710 generates the following capacityconstraints:

C _(a)(k)−P _(bat)(k)Δt≤C _(rated)

C _(a)(k)−P _(bat)(k)Δt≥0

where C_(a)(k) is the available battery capacity (e.g., kWh) at thebeginning of time step k, P_(bat)(k) is the rate at which battery unit1502 is discharged during time step k (e.g., kW), Δt is the duration ofeach time step, and C_(rated) is the maximum rated capacity of batteryunit 1502 (e.g., kWh). The term P_(bat)(k)Δt represents the change inbattery capacity during time step k. These capacity constraints ensurethat the capacity of battery unit 1502 is maintained between zero andthe maximum rated capacity C_(rated).

In some embodiments, economic controller 1710 generates and imposes oneor more capacity constraints on the operation of powered cooling towercomponents 1602. For example, powered cooling tower components 1602 mayhave a maximum operating point (e.g., a maximum pump speed, a maximumcooling capacity, etc.) which corresponds to a maximum power consumptionP_(total,max). Economic controller 1710 can be configured to generate aconstraint which limits the power P_(total) provided to powered coolingtower components 1602 between zero and the maximum power consumptionP_(total,max) as shown in the following equation:

0≤P _(total) ≤P _(total,max)

P _(total) =P _(sp,grid) +P _(sp,bat)

where the total power P_(total) provided to powered cooling towercomponents 1602 is the sum of the grid power setpoint P_(sp,grid) andthe battery power setpoint P_(sp,bat).

Economic controller 1710 can optimize the predictive cost function Jsubject to the constraints to determine optimal values for the decisionvariables P_(total), P_(fan), P_(pump), P_(grid), and P_(bat), whereP_(total)=P_(bat)+P_(grid)+P_(PV). In some embodiments, economiccontroller 1710 uses the optimal values for P_(total), P_(bat), and/orP_(grid) to generate power setpoints for tracking controller 1712. Thepower setpoints can include battery power setpoints P_(sp,bat), gridpower setpoints P_(sp,grid), and/or cooling tower power setpointsP_(sp,total) for each of the time steps k in the optimization period.Economic controller 1710 can provide the power setpoints to trackingcontroller 1712.

Tracking Controller

Tracking controller 1712 can use the optimal power setpointsP_(sp,grid), P_(sp,bat), and/or P_(sp,total) generated by economiccontroller 1710 to determine optimal temperature setpoints (e.g., a sumpwater temperature setpoint T_(sp,sump), a condenser water temperaturesetpoint T_(sp,cond), etc.) and an optimal battery charge or dischargerate (i.e., Bat_(C/D)). In some embodiments, tracking controller 1712generates a sump water temperature setpoint T_(sp,sump) and/or acondenser water temperature setpoint T_(sp,cond) that are predicted toachieve the power setpoint P_(sp,total) for cooling tower 1512. In otherwords, tracking controller 1712 may generate a sump water temperaturesetpoint T_(sp,sump) and/or a condenser water temperature setpointT_(sp,cond) that cause cooling tower 1512 to consume the optimal amountof power P_(total) determined by economic controller 1710.

In some embodiments, tracking controller 1712 relates the powerconsumption of cooling tower 1512 to the sump water temperature T_(sump)and the sump water temperature setpoint T_(sp,sump) using a powerconsumption model. For example, tracking controller 1712 can use a modelof equipment controller 1714 to determine the control action performedby equipment controller 1714 as a function of the sump water temperatureT_(sump) and the sump water temperature setpoint T_(sp,sump). An exampleof such a zone regulatory controller model is shown in the followingequation:

P _(total)=ƒ₄(T _(sump) ,T _(sp,sump))

The function ƒ₄ can be identified from data. For example, trackingcontroller 1712 can collect measurements of P_(total) and T_(sump) andidentify the corresponding value of T_(sp,sump). Tracking controller1712 can perform a system identification process using the collectedvalues of P_(total), T_(sump), and T_(sp,sump) as training data todetermine the function ƒ₄ that defines the relationship between suchvariables.

Tracking controller 1712 may use a similar model to determine therelationship between the total power consumption P_(total) of coolingtower 1512 and the condenser water temperature setpoint T_(sp,cond). Forexample, tracking controller 1712 can define the power consumptionP_(total) of cooling tower 1512 as a function of the condenser watertemperature T_(cond) and the condenser water temperature setpointT_(sp,cond). An example of such a model is shown in the followingequation:

P _(total)=ƒ₅(T _(cond) ,T _(sp,cond))

The function ƒ₅ can be identified from data. For example, trackingcontroller 1712 can collect measurements of P_(total) and T_(cond) andidentify the corresponding value of T_(sp,cond). Tracking controller1712 can perform a system identification process using the collectedvalues of P_(total), T_(cond), and T_(sp,cond) as training data todetermine the function ƒ₅ that defines the relationship between suchvariables.

Tracking controller 1712 can use the relationships between P_(total),T_(sp,sump), and T_(sp,cond) to determine values for T_(sp,sump) andT_(sp,cond). For example, tracking controller 1712 can receive the valueof P_(total) as an input from economic controller 1710 (i.e.,P_(sp,total)) and can use determine corresponding values of T_(sp,sump)and T_(sp,cond). Tracking controller 1712 can provide the values ofT_(sp,sump) and T_(sp,cond) as outputs to equipment controller 1714.

In some embodiments, tracking controller 1712 uses the battery powersetpoint P_(sp,bat) to determine the optimal rate Bat_(C/D) at which tocharge or discharge battery unit 1502. For example, the battery powersetpoint P_(sp,bat) may define a power value (kW) which can betranslated by tracking controller 1712 into a control signal for powerinverter 1610 and/or equipment controller 1714. In other embodiments,the battery power setpoint P_(sp,bat) is provided directly to powerinverter 1610 and used by power inverter 1610 to control the batterypower P_(bat).

Equipment Controller

Equipment controller 1714 can use the optimal temperature setpointsT_(sp,sump) or T_(sp,cond) generated by tracking controller 1712 togenerate control signals for powered cooling tower components 1602. Thecontrol signals generated by equipment controller 1714 may drive theactual (e.g., measured) temperatures T_(sump) and/or T_(cond) to thesetpoints. Equipment controller 1714 can use any of a variety of controltechniques to generate control signals for powered cooling towercomponents 1602. For example, equipment controller 1714 can usestate-based algorithms, extremum seeking control (ESC) algorithms,proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, or other feedback controlalgorithms, to generate control signals for powered cooling towercomponents 1602.

The control signals may include on/off commands, speed setpoints for fan1514, pressure setpoints or flow rate setpoints for pump 1516, or othertypes of setpoints for individual devices of powered cooling towercomponents 1602. In other embodiments, the control signals may includethe temperature setpoints (e.g., a sump water temperature setpointT_(sp,sump), a condenser water temperature setpoint T_(sp,cond), etc.)generated by predictive cooling tower controller 1504. The temperaturesetpoints can be provided to powered cooling tower components 1602 orlocal controllers for powered cooling tower components 1602 whichoperate to achieve the temperature setpoints. For example, a localcontroller for fan 1514 may receive a measurement of the sump watertemperature T_(sump) and/or a measurement the condenser watertemperature T_(cond) from temperature sensors 1716 and can modulate thespeed of fan 1514 to drive the measured temperatures to the setpoints.

In some embodiments, equipment controller 1714 is configured to providecontrol signals to power inverter 1610. The control signals provided topower inverter 1610 can include a battery power setpoint P_(sp,bat)and/or the optimal charge/discharge rate Bat_(C/D). Equipment controller1714 can be configured to operate power inverter 1610 to achieve thebattery power setpoint P_(sp,bat). For example, equipment controller1714 can cause power inverter 1610 to charge battery unit 1502 ordischarge battery unit 1502 in accordance with the battery powersetpoint P_(sp,bat).

Valve Unit With Battery and Predictive Control

Referring now to FIGS. 18-19, a valve unit 1800 with a battery unit 1802and predictive valve controller 1804 is shown, according to someembodiments. Valve unit 1800 can be configured to control a valve 1832via a valve actuator 1834. Valve 1832 can be a fluid control valveconfigured to control the flowrate of fluid from an inlet pipe 1812 toan outlet pipe 1814. Actuator 1834 may include a motor or other poweredcomponent configured to modulate the position of valve 1832. In someembodiments, valve unit 1800 is configured to control the flow of fluidthrough a HVAC device 1836 via a fluid circuit 1838. HVAC device 1836may include, for example, a heating coil or cooling coil, an airhandling unit, a rooftop unit, a heat exchanger, a refrigerator orfreezer, a condenser or evaporator, a cooling tower, or any other typeof system or device that receives a fluid in a HVAC system.

In some embodiments, battery unit 1802 includes one or more batterycells 1806. Battery cells 1806 are configured to store and dischargeelectric energy (i.e., electricity). In some embodiments, battery unit1802 is charged using electricity from an external energy grid (e.g.,provided by an electric utility). The electricity stored in battery unit1802 can be discharged to power one or more powered components of valveunit 1800 (e.g., actuator 1834). Advantageously, battery unit 1802allows valve unit 1800 to draw electricity from the energy grid andcharge battery unit 1802 when energy prices are low and discharge thestored electricity when energy prices are high to time-shift theelectric load of valve unit 1800. In some embodiments, battery unit 1802has sufficient energy capacity to power valve unit 1800 forapproximately 4-6 hours when operating at maximum capacity such thatbattery unit 1802 can be utilized during high energy cost periods andcharged during low energy cost periods.

In some embodiments, predictive valve controller 1804 performs anoptimization process to determine whether to charge or discharge batteryunit 1802 during each of a plurality of time steps that occur during anoptimization period. Predictive valve controller 1804 may use weatherand pricing data 1810 to predict the amount of heating/cooling requiredand the cost of electricity during each of the plurality of time steps.Predictive valve controller 1804 can optimize an objective function thataccounts for the cost of electricity purchased from the energy grid overthe duration of the optimization period. Predictive valve controller1804 can determine an amount of electricity to purchase from the energygrid and an amount of electricity to store or discharge from batteryunit 1802 during each time step. The objective function and theoptimization performed by predictive valve controller 1804 are describedin greater detail with reference to FIGS. 20-21.

Predictive Valve Control System

Referring now to FIG. 20, a block diagram of a predictive valve controlsystem 2000 is shown, according to some embodiments. Several of thecomponents shown in control system 2000 may be part of valve unit 1800.For example, valve unit 1800 may include actuator 1834, battery unit1802, predictive valve controller 1804, power inverter 2010, and a powerjunction 2012.

Power inverter 2010 may be configured to convert electric power betweendirect current (DC) and alternating current (AC). For example, batteryunit 1802 may be configured to store and output DC power, whereas energygrid 2014 and actuator 1834 may be configured to consume and provide ACpower. Power inverter 2010 may be used to convert DC power from batteryunit 1802 into a sinusoidal AC output synchronized to the grid frequencyof energy grid 2014 and/or actuator 1834. Power inverter 2010 may alsobe used to convert AC power from energy grid 2014 into DC power that canbe stored in battery unit 1802. The power output of battery unit 1802 isshown as P_(bat). P_(bat) may be positive if battery unit 1802 isproviding power to power inverter 2010 (i.e., battery unit 1802 isdischarging) or negative if battery unit 1802 is receiving power frompower inverter 2010 (i.e., battery unit 1802 is charging).

In some instances, power inverter 2010 receives a DC power output frombattery unit 1802 and converts the DC power output to an AC power outputthat can be provided to actuator 1834. Power inverter 2010 maysynchronize the frequency of the AC power output with that of energygrid 2014 (e.g., 50 Hz or 60 Hz) using a local oscillator and may limitthe voltage of the AC power output to no higher than the grid voltage.In some embodiments, power inverter 2010 is a resonant inverter thatincludes or uses LC circuits to remove the harmonics from a simplesquare wave in order to achieve a sine wave matching the frequency ofenergy grid 2014. In various embodiments, power inverter 2010 mayoperate using high-frequency transformers, low-frequency transformers,or without transformers. Low-frequency transformers may convert the DCoutput from battery unit 1802 directly to the AC output provided toactuator 1834. High-frequency transformers may employ a multi-stepprocess that involves converting the DC output to high-frequency AC,then back to DC, and then finally to the AC output provided to actuator1834.

Power junction 2012 is the point at which actuator 1834, energy grid2014, and power inverter 2010 are electrically connected. The powersupplied to power junction 2012 from power inverter 2010 is shown asP_(bat). P_(bat) may be positive if power inverter 2010 is providingpower to power junction 2012 (i.e., battery unit 1802 is discharging) ornegative if power inverter 2010 is receiving power from power junction2012 (i.e., battery unit 1802 is charging). The power supplied to powerjunction 2012 from energy grid 2014 is shown as P_(grid). P_(bat) andP_(gird) combine at power junction 2012 to form P_(total) (i.e.,P_(total)=P_(grid)+P_(bat)). P_(total) may be defined as the powerprovided to actuator 1834 from power junction 2012. In some instances,P_(total) is greater than P_(grid). For example, when battery unit 1802is discharging, P_(bat) may be positive which adds to the grid powerP_(grid) when P_(bat) combines with P_(grid) to form P_(total). In otherinstances, P_(total) may be less than P_(grid). For example, whenbattery unit 1802 is charging, P_(bat) may be negative which subtractsfrom the grid power P_(grid) when P_(bat) and P_(grid) combine to formP_(total).

Predictive valve controller 1804 can be configured to control actuator1834 and power inverter 2010. In some embodiments, predictive valvecontroller 1804 generates and provides a battery power setpointP_(sp,bat) to power inverter 2010. The battery power setpoint P_(sp,bat)may include a positive or negative power value (e.g., kW) which causespower inverter 2010 to charge battery unit 1802 (when P_(sp,bat) isnegative) using power available at power junction 2012 or dischargebattery unit 1802 (when P_(sp,bat) is positive) to provide power topower junction 2012 in order to achieve the battery power setpointP_(sp,bat).

In some embodiments, predictive valve controller 1804 generates andprovides control signals to actuator 1834. Predictive valve controller1804 may use a multi-stage optimization technique to generate thecontrol signals. For example, predictive valve controller 1804 mayinclude an economic controller configured to determine the optimalamount of power to be consumed by actuator 1834 at each time step duringthe optimization period. The optimal amount of power to be consumed mayminimize a cost function that accounts for the cost of energy consumedby valve unit 1800. The cost of energy may be based on time-varyingenergy prices from electric utility 2018. In some embodiments,predictive valve controller 1804 determines an optimal amount of powerto purchase from energy grid 2014 (i.e., a grid power setpointP_(sp,grid)) and an optimal amount of power to store or discharge frombattery unit 1802 (i.e., a battery power setpoint P_(sp,bat)) at each ofthe plurality of time steps. Predictive valve controller 1804 maymonitor the actual power usage of actuator 1834 and may utilize theactual power usage as a feedback signal when generating the optimalpower setpoints.

Predictive valve controller 1804 may include a tracking controllerconfigured to generate position setpoints for actuator 1834 that achievethe optimal amount of power consumption at each time step. In someembodiments, predictive valve controller 1804 uses an equipment modelfor actuator 1834 to determine an a position of actuator 1834 thatcorresponds to the optimal amount of power consumption.

In some embodiments, predictive valve controller 1804 uses the positionsetpoints to generate the control signals for actuator 1834. The controlsignals may include on/off commands, position commands, voltage signals,or other types of setpoints that affect the operation of actuator 1834.In other embodiments, the control signals may include the positionsetpoints generated by predictive valve controller 1804. The setpointscan be provided to actuator 1834 or local controllers for actuator 1834which operate to achieve the setpoints. For example, a local controllerfor actuator 1834 may receive a measurement of the valve position fromone or more position sensors. The local controller can use a feedbackcontrol process (e.g., PID, ESC, MPC, etc.) to adjust the position ofactuator 1834 and/or valve 1832 to drive the measured position to thesetpoint(s). The multi-stage optimization performed by predictive valvecontroller 1804 is described in greater detail with reference to FIG.21.

Predictive Valve Controller

Referring now to FIG. 21, a block diagram illustrating predictive valvecontroller 1804 in greater detail is shown, according to an exemplaryembodiment. Predictive valve controller 1804 is shown to include acommunications interface 2102 and a processing circuit 2104.Communications interface 2102 may facilitate communications betweencontroller 1804 and external systems or devices. For example,communications interface 2102 may receive measurements of the valveposition from position sensors 2118 and measurements of the power usageof actuator 1834. In some embodiments, communications interface 2102receives measurements of the state-of-charge (SOC) of battery unit 1802,which can be provided as a percentage of the maximum battery capacity(i.e., battery %). Communications interface 2102 can receive weatherforecasts from a weather service 916 and predicted energy costs anddemand costs from an electric utility 2018. In some embodiments,predictive valve controller 1804 uses communications interface 2102 toprovide control signals actuator 1834 and power inverter 2010.

Communications interface 2102 may include wired or wirelesscommunications interfaces (e.g., jacks, antennas, transmitters,receivers, transceivers, wire terminals, etc.) for conducting datacommunications external systems or devices. In various embodiments, thecommunications may be direct (e.g., local wired or wirelesscommunications) or via a communications network (e.g., a WAN, theInternet, a cellular network, etc.). For example, communicationsinterface 2102 can include an Ethernet card and port for sending andreceiving data via an Ethernet-based communications link or network. Inanother example, communications interface 2102 can include a Wi-Fitransceiver for communicating via a wireless communications network orcellular or mobile phone communications transceivers.

Processing circuit 2104 is shown to include a processor 2106 and memory2108. Processor 2106 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 2106 isconfigured to execute computer code or instructions stored in memory2108 or received from other computer readable media (e.g., CDROM,network storage, a remote server, etc.).

Memory 2108 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 2108 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory2108 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 2108 may be communicably connected toprocessor 2106 via processing circuit 2104 and may include computer codefor executing (e.g., by processor 2106) one or more processes describedherein. When processor 2106 executes instructions stored in memory 2108for completing the various activities described herein, processor 2106generally configures controller 1804 (and more particularly processingcircuit 2104) to complete such activities.

Still referring to FIG. 21, predictive valve controller 1804 is shown toinclude an economic controller 2110, a tracking controller 2112, and anequipment controller 2114. Controllers 2110-2114 can be configured toperform a multi-state optimization process to generate control signalsfor power inverter 2010 and actuator 1834. In brief overview, economiccontroller 2110 can optimize a predictive cost function to determine anoptimal amount of power to purchase from energy grid 2014 (i.e., a gridpower setpoint P_(sp,grid)), an optimal amount of power to store ordischarge from battery unit 1802 (i.e., a battery power setpointP_(sp,bat)), and/or an optimal amount of power to be consumed byactuator 1834 (i.e., a pump power setpoint P_(sp,act)) at each time stepof an optimization period. Tracking controller 2112 can use the optimalpower setpoints P_(sp,grid), P_(sp,bat), and/or P_(sp,act) to determineoptimal position setpoints Pos_(sp) for valve 1832 and an optimalbattery charge or discharge rate (i.e., Bat_(C/D)). Equipment controller2114 can use the optimal position setpoints Pos_(sp) to generate controlsignals for actuator 1834 that drive the actual (e.g., measured)position to the setpoints (e.g., using a feedback control technique).Each of controllers 2110-2114 is described in detail below.

Economic Controller

Economic controller 2110 can be configured to optimize a predictive costfunction to determine an optimal amount of power to purchase from energygrid 2014 (i.e., a grid power setpoint P_(sp,grid)), an optimal amountof power to store or discharge from battery unit 1802 (i.e., a batterypower setpoint P_(sp,bat)), and/or an optimal amount of power to beconsumed by actuator 1834 (i.e., an actuator power setpoint P_(sp,act))at each time step of an optimization period. An example of a predictivecost function which can be optimized by economic controller 2110 isshown in the following equation:

${\min (J)} = {{\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{act}(k)}\Delta \; t}} + {C_{DC}{\max\limits_{k}\left( {P_{grid}(k)} \right)}} - {\sum\limits_{k = 1}^{h}{{C_{ec}(k)}{P_{bat}(k)}\Delta \; t}}}$

where C_(ec)(k) is the cost per unit of electricity (e.g., $/kWh)purchased from electric utility 2018 during time step k, P_(act)(k) isthe power consumption of actuator 1834 at time step k, C_(DC) is thedemand charge rate (e.g., $/kW), where the max( ) term selects themaximum electricity purchase of valve unit 1800 (i.e., the maximum valueof P_(grid)(k)) during any time step k of the optimization period,P_(bat)(k) is the amount of power discharged from battery unit 1802during time step k, and Δt is the duration of each time step k. Economiccontroller 2110 can optimize the predictive cost function J over theduration of the optimization period (e.g., from time step k=1 to timestep k=h) to predict the total cost of operating valve unit 1800 overthe duration of the optimization period.

The first term of the predictive cost function J represents the cost ofelectricity consumed by actuator 1834 over the duration of theoptimization period. The values of the parameter C_(ec)(k) at each timestep k can be defined by the energy cost information provided byelectric utility 2018. In some embodiments, the cost of electricityvaries as a function of time, which results in different values ofC_(ec)(k) at different time steps k. The variable P_(act)(k) is adecision variable which can be optimized by economic controller 2110.

The second term of the predictive cost function J represents the demandcharge. Demand charge is an additional charge imposed by some utilityproviders based on the maximum power consumption during an applicabledemand charge period. For example, the demand charge rate C_(DC) may bespecified in terms of dollars per unit of power (e.g., $/kW) and may bemultiplied by the peak power usage (e.g., kW) during a demand chargeperiod to calculate the demand charge. In the predictive cost functionJ, the demand charge rate C_(DC) may be defined by the demand costinformation received from electric utility 2018. The variableP_(grid)(k) is a decision variable which can be optimized by economiccontroller 2110 in order to reduce the peak power usage max(P_(grid)(k))that occurs during the demand charge period. Load shifting may alloweconomic controller 2110 to smooth momentary spikes in the electricdemand of valve unit 1800 by storing energy in battery unit 1802 whenthe power consumption of actuator 1834 is low. The stored energy can bedischarged from battery unit 1802 when the power consumption of actuator1834 is high in order to reduce the peak power draw P_(grid) from energygrid 2014, thereby decreasing the demand charge incurred.

The final term of the predictive cost function J represents the costsavings resulting from the use of battery unit 1802. Unlike the previousterms in the cost function J, the final term subtracts from the totalcost. The values of the parameter C_(ec)(k) at each time step k can bedefined by the energy cost information provided by electric utility2018. In some embodiments, the cost of electricity varies as a functionof time, which results in different values of C_(ec)(k) at differenttime steps k. The variable P_(bat)(k) is a decision variable which canbe optimized by economic controller 2110. A positive value of P_(bat)(k)indicates that battery unit 1802 is discharging, whereas a negativevalue of P_(bat)(k) indicates that battery unit 1802 is charging. Thepower discharged from battery unit 1802 P_(bat)(k) can be used tosatisfy some or all of the total power consumption P_(total)(k) ofactuator 1834, which reduces the amount of power P_(grid)(k) purchasedfrom energy grid 2014 (i.e., P_(grid)(k)=P_(total)(k)−P_(bat)(k)).However, charging battery unit 1802 results in a negative value ofP_(bat)(k) which adds to the total amount of power P_(grid)(k) purchasedfrom energy grid 2014.

Economic controller 2110 can optimize the predictive cost function Jover the duration of the optimization period to determine optimal valuesof the decision variables at each time step during the optimizationperiod. In some embodiments, the optimization period has a duration ofapproximately one day and each time step is approximately fifteenminutes. However, the durations of the optimization period and the timesteps can vary in other embodiments and can be adjusted by a user.Advantageously, economic controller 2110 can use battery unit 1802 toperform load shifting by drawing electricity from energy grid 2014 whenenergy prices are low and/or when the power consumed by actuator 1834 islow. The electricity can be stored in battery unit 1802 and dischargedlater when energy prices are high and/or the power consumption ofactuator 1834 is high. This enables economic controller 2110 to reducethe cost of electricity consumed by valve unit 1800 and can smoothmomentary spikes in the electric demand of valve unit 1800, therebyreducing the demand charge incurred.

Economic controller 2110 can be configured to impose constraints on theoptimization of the predictive cost function J. In some embodiments, theconstraints include constraints on the position of actuator 1834.Economic controller 2110 can be configured to maintain the actual orpredicted position between a minimum position bound Pos_(min) and amaximum position bound Pos_(max) (i.e., Pos_(min)≤Pos≤Pos_(max)) at alltimes. The parameters Pos_(min) and Pos_(max) may be time-varying todefine different position ranges at different times.

In addition to constraints on the position of valve 1832, economiccontroller 2110 can impose constraints on the state-of-charge (SOC) andcharge/discharge rates of battery unit 1802. In some embodiments,economic controller 2110 generates and imposes the following powerconstraints on the predictive cost function J:

P _(bat) ≤P _(rated) −P _(bat) ≤P _(rated)

where P_(bat) is the amount of power discharged from battery unit 1802and P_(rated) is the rated battery power of battery unit 1802 (e.g., themaximum rate at which battery unit 1802 can be charged or discharged).These power constraints ensure that battery unit 1802 is not charged ordischarged at a rate that exceeds the maximum possible batterycharge/discharge rate P_(rated).

In some embodiments, economic controller 2110 generates and imposes oneor more capacity constraints on the predictive cost function J Thecapacity constraints may be used to relate the battery power P_(bat)charged or discharged during each time step to the capacity and SOC ofbattery unit 1802. The capacity constraints may ensure that the capacityof battery unit 1802 is maintained within acceptable lower and upperbounds at each time step of the optimization period. In someembodiments, economic controller 2110 generates the following capacityconstraints:

C _(a)(k)−P _(bat)(k)Δt≤C _(rated)

C _(a)(k)−P _(bat)(k)Δt≥0

where C_(a)(k) is the available battery capacity (e.g., kWh) at thebeginning of time step k, P_(bat)(k) is the rate at which battery unit1802 is discharged during time step k (e.g., kW), Δt is the duration ofeach time step, and C_(rated) is the maximum rated capacity of batteryunit 1802 (e.g., kWh). The term P_(bat)(k)Δt represents the change inbattery capacity during time step k. These capacity constraints ensurethat the capacity of battery unit 1802 is maintained between zero andthe maximum rated capacity C_(rated).

In some embodiments, economic controller 2110 generates and imposes oneor more capacity constraints on the operation of actuator 1834. Forexample, actuator 1834 may have a maximum operating point (e.g., amaximum actuation speed, a maximum position, etc.) which corresponds toa maximum power consumption P_(act,max). Economic controller 2110 can beconfigured to generate a constraint which limits the power P_(act)provided to actuator 1834 between zero and the maximum power consumptionP_(act,max) as shown in the following equation:

0≤P _(act) ≤P _(act,max)

P _(act) =P _(sp,grid) +P _(sp,bat)

where the total power P_(act) provided to actuator 1834 is the sum ofthe grid power setpoint P_(sp,grid) and the battery power setpointP_(sp,bat).

Economic controller 2110 can optimize the predictive cost function Jsubject to the constraints to determine optimal values for the decisionvariables P_(act), P_(grid), and P_(bat), whereP_(act)=P_(bat)+P_(grid). In some embodiments, economic controller 2110uses the optimal values for P_(act), P_(bat), and/or P_(grid) togenerate power setpoints for tracking controller 2112. The powersetpoints can include battery power setpoints P_(sp,bat), grid powersetpoints P_(sp,grid), and/or actuator power setpoints P_(sp,act) foreach of the time steps k in the optimization period. Economic controller2110 can provide the power setpoints to tracking controller 2112.

Tracking Controller

Tracking controller 2112 can use the optimal power setpointsP_(sp,grid), P_(sp,bat), and/or P_(sp,act) generated by economiccontroller 2110 to determine optimal position setpoints Pos_(sp) and anoptimal battery charge or discharge rate (i.e., Bat_(C/D)). In someembodiments, tracking controller 2112 generates a position setpointPos_(sp) predicted to achieve the power setpoint P_(sp,act) for actuator1834. In other words, tracking controller 2112 may generate a positionsetpoint Pos_(sp) that causes actuator 1834 to consume the optimalamount of power P_(act) determined by economic controller 2110.

In some embodiments, tracking controller 2112 uses the battery powersetpoint P_(sp,bat) to determine the optimal rate Bat_(C/D) at which tocharge or discharge battery unit 1802. For example, the battery powersetpoint P_(sp,bat) may define a power value (kW) which can betranslated by tracking controller 2112 into a control signal for powerinverter 2010 and/or equipment controller 2114. In other embodiments,the battery power setpoint P_(sp,bat) is provided directly to powerinverter 2010 and used by power inverter 2010 to control the batterypower P_(bat).

Equipment Controller

Equipment controller 2114 can use the optimal position setpointsPos_(sp) generated by tracking controller 2112 to generate controlsignals for actuator 1834. The control signals generated by equipmentcontroller 2114 may drive the actual (e.g., measured) position of valve1832 the setpoints. Equipment controller 2114 can use any of a varietyof control techniques to generate control signals for actuator 1834. Forexample, equipment controller 2114 can use state-based algorithms,extremum seeking control (ESC) algorithms, proportional-integral (PI)control algorithms, proportional-integral-derivative (PID) controlalgorithms, model predictive control (MPC) algorithms, or other feedbackcontrol algorithms, to generate control signals for actuator 1834.

The control signals may include on/off commands, position commands,voltage signals, or other types of setpoints that affect the operationof actuator 1834. In other embodiments, the control signals may includethe position setpoints generated by predictive valve controller 1804.The setpoints can be provided to actuator 1834 or local controllers foractuator 1834 which operate to achieve the setpoints. For example, alocal controller for actuator 1834 may receive a measurement of thevalve position from one or more position sensors. The local controllercan use a feedback control process (e.g., PID, ESC, MPC, etc.) to adjustthe position of actuator 1834 and/or valve 1832 to drive the measuredposition to the setpoint.

In some embodiments, equipment controller 2114 is configured to providecontrol signals to power inverter 2010. The control signals provided topower inverter 2010 can include a battery power setpoint P_(sp,bat)and/or the optimal charge/discharge rate Bat_(C/D). Equipment controller2114 can be configured to operate power inverter 2010 to achieve thebattery power setpoint P_(sp,bat). For example, equipment controller2114 can cause power inverter 2010 to charge battery unit 1802 ordischarge battery unit 1802 in accordance with the battery powersetpoint P_(sp,bat).

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A central energy facility (CEF) comprising: aplurality of powered CEF components comprising a chiller unit and acooling tower; a battery unit configured to store electric energy froman energy grid and discharge the stored electric energy for use inpowering the powered CEF components; and a predictive CEF controllerconfigured to optimize a predictive cost function to determine anoptimal amount of electric energy to purchase from the energy grid andan optimal amount of electric energy to store in the battery unit ordischarge from the battery unit for use in powering the powered CEFcomponents at each time step of an optimization period.
 2. The CEF ofclaim 1, further comprising one or more photovoltaic panels configuredto collect photovoltaic energy; wherein the predictive CEF controller isconfigured to determine an optimal amount of the photovoltaic energy tostore in the battery unit and an optimal amount of the photovoltaicenergy to be consumed by the powered CEF components at each time step ofthe optimization period.
 3. The CEF of claim 1, wherein the predictivecost function accounts for: a cost of the electric energy purchased fromthe energy grid at each time step of the optimization period; and a costsavings resulting from discharging stored electric energy from thebattery unit at each time step of the optimization period.
 4. The CEF ofclaim 1, wherein the predictive CEF controller is configured to: receiveenergy pricing data defining a cost per unit of electric energypurchased from the energy grid at each time step of the optimizationperiod; and use the energy pricing data as inputs to the predictive costfunction.
 5. The CEF of claim 1, wherein the predictive cost functionaccounts for a demand charge based on a maximum power consumption of theCEF during a demand charge period that overlaps at least partially withthe optimization period; wherein the predictive CEF controller isconfigured to receive energy pricing data defining the demand charge andto use the energy pricing data as inputs to the predictive costfunction.
 6. The CEF of claim 1, wherein the predictive CEF controllercomprises: an economic controller configured to determine optimal powersetpoints for the powered CEF components and for the battery unit ateach time step of the optimization period; a tracking controllerconfigured to use the optimal power setpoints to determine optimaltemperature setpoints at each time step of the optimization period; andan equipment controller configured to use the optimal temperaturesetpoints to generate control signals for the powered CEF components andfor the battery unit at each time step of the optimization period.
 7. Anair-cooled chiller unit comprising: a refrigeration circuit comprisingan evaporator and a condenser; a plurality of powered chiller componentscomprising a compressor configured to circulate a refrigerant throughthe refrigeration circuit and a fan configured to provide cooling forthe condenser; a battery unit configured to store electric energy froman energy grid and discharge the stored electric energy for use inpowering the powered chiller components; and a predictive chillercontroller configured to optimize a predictive cost function todetermine an optimal amount of electric energy to purchase from theenergy grid and an optimal amount of electric energy to store in thebattery unit or discharge from the battery unit for use in powering thepowered chiller components at each time step of an optimization period.8. The air-cooled chiller unit of claim 7, further comprising one ormore photovoltaic panels configured to collect photovoltaic energy;wherein the predictive chiller controller is configured to determine anoptimal amount of the photovoltaic energy to store in the battery unitand an optimal amount of the photovoltaic energy to be consumed by thepowered chiller components at each time step of the optimization period.9. The air-cooled chiller unit of claim 7, wherein the predictive costfunction accounts for: a cost of the electric energy purchased from theenergy grid at each time step of the optimization period; and a costsavings resulting from discharging stored electric energy from thebattery unit at each time step of the optimization period.
 10. Theair-cooled chiller unit of claim 7, wherein the predictive chillercontroller is configured to: receive energy pricing data defining a costper unit of electric energy purchased from the energy grid at each timestep of the optimization period; and use the energy pricing data asinputs to the predictive cost function.
 11. The air-cooled chiller unitof claim 7, wherein the predictive cost function accounts for a demandcharge based on a maximum power consumption of the air-cooled chillerunit during a demand charge period that overlaps at least partially withthe optimization period; wherein the predictive chiller controller isconfigured to receive energy pricing data defining the demand charge andto use the energy pricing data as inputs to the predictive costfunction.
 12. The air-cooled chiller unit of claim 7, wherein thepredictive chiller controller comprises: an economic controllerconfigured to determine optimal power setpoints for the powered chillercomponents and for the battery unit at each time step of theoptimization period; a tracking controller configured to use the optimalpower setpoints to determine optimal temperature setpoints at each timestep of the optimization period; and an equipment controller configuredto use the optimal temperature setpoints to generate control signals forthe powered chiller components and for the battery unit at each timestep of the optimization period.
 13. A pump unit comprising: a pumpconfigured to circulate a fluid through a fluid circuit; a battery unitconfigured to store electric energy from an energy grid and dischargethe stored electric energy for use in powering the pump; and apredictive pump controller configured to optimize a predictive costfunction to determine an optimal amount of electric energy to purchasefrom the energy grid and an optimal amount of electric energy to storein the battery unit or discharge from the battery unit for use inpowering the pump at each time step of an optimization period.
 14. Thepump unit of claim 13, wherein the predictive cost function accountsfor: a cost of the electric energy purchased from the energy grid ateach time step of the optimization period; and a cost savings resultingfrom discharging stored electric energy from the battery unit at eachtime step of the optimization period.
 15. The pump unit of claim 13,wherein the predictive pump controller is configured to: receive energypricing data defining a cost per unit of electric energy purchased fromthe energy grid at each time step of the optimization period; and usethe energy pricing data as inputs to the predictive cost function. 16.The pump unit of claim 13, wherein the predictive cost function accountsfor a demand charge based on a maximum power consumption of the pumpunit during a demand charge period that overlaps at least partially withthe optimization period; wherein the predictive pump controller isconfigured to receive energy pricing data defining the demand charge andto use the energy pricing data as inputs to the predictive costfunction.
 17. The pump unit of claim 13, wherein the predictive pumpcontroller comprises: an economic controller configured to determineoptimal power setpoints for the pump and for the battery unit at eachtime step of the optimization period; a tracking controller configuredto use the optimal power setpoints to determine optimal flow setpointsor pressure setpoints at each time step of the optimization period; andan equipment controller configured to use the optimal flow setpoints orpressure setpoints to generate control signals for the pump and for thebattery unit at each time step of the optimization period.
 18. A coolingtower unit comprising: one or more powered cooling tower componentscomprising at least one of a fan and a pump; a battery unit configuredto store electric energy from an energy grid and discharge the storedelectric energy for use in powering the powered cooling towercomponents; and a predictive cooling tower controller configured tooptimize a predictive cost function to determine an optimal amount ofelectric energy to purchase from the energy grid and an optimal amountof electric energy to store in the battery unit or discharge from thebattery unit for use in powering the powered cooling tower components ateach time step of an optimization period.
 19. The cooling tower unit ofclaim 18, further comprising one or more photovoltaic panels configuredto collect photovoltaic energy; wherein the predictive cooling towercontroller is configured to determine an optimal amount of thephotovoltaic energy to store in the battery unit and an optimal amountof the photovoltaic energy to be consumed by the powered cooling towercomponents at each time step of the optimization period.
 20. The coolingtower unit of claim 18, wherein the predictive cost function accountsfor: a cost of the electric energy purchased from the energy grid ateach time step of the optimization period; and a cost savings resultingfrom discharging stored electric energy from the battery unit at eachtime step of the optimization period.
 21. The cooling tower unit ofclaim 18, wherein the predictive cooling tower controller is configuredto: receive energy pricing data defining a cost per unit of electricenergy purchased from the energy grid at each time step of theoptimization period; and use the energy pricing data as inputs to thepredictive cost function.
 22. The cooling tower unit of claim 18,wherein the predictive cost function accounts for a demand charge basedon a maximum power consumption of the cooling tower unit during a demandcharge period that overlaps at least partially with the optimizationperiod; wherein the predictive cooling tower controller is configured toreceive energy pricing data defining the demand charge and to use theenergy pricing data as inputs to the predictive cost function.
 23. Thecooling tower unit of claim 18, wherein the predictive cooling towercontroller comprises: an economic controller configured to determineoptimal power setpoints for the powered cooling tower components and forthe battery unit at each time step of the optimization period; atracking controller configured to use the optimal power setpoints todetermine optimal temperature setpoints at each time step of theoptimization period; and an equipment controller configured to use theoptimal temperature setpoints to generate control signals for thepowered cooling tower components and for the battery unit at each timestep of the optimization period.
 24. A valve unit comprising: a valveconfigured to control a flowrate of a fluid through a fluid conduit; oneor more powered valve components comprising a valve actuator coupled tothe valve and configured to modulate a position of the valve; a batteryunit configured to store electric energy from an energy grid anddischarge the stored electric energy for use in powering the poweredvalve components; and a predictive valve controller configured tooptimize a predictive cost function to determine an optimal amount ofelectric energy to purchase from the energy grid and an optimal amountof electric energy to store in the battery unit or discharge from thebattery unit for use in powering the powered valve components at eachtime step of an optimization period.
 25. The valve unit of claim 24,wherein the predictive cost function accounts for: a cost of theelectric energy purchased from the energy grid at each time step of theoptimization period; and a cost savings resulting from dischargingstored electric energy from the battery unit at each time step of theoptimization period.
 26. The valve unit of claim 24, wherein thepredictive valve controller is configured to: receive energy pricingdata defining a cost per unit of electric energy purchased from theenergy grid at each time step of the optimization period; and use theenergy pricing data as inputs to the predictive cost function.
 27. Thevalve unit of claim 24, wherein the predictive cost function accountsfor a demand charge based on a maximum power consumption of the valveunit during a demand charge period that overlaps at least partially withthe optimization period; wherein the predictive valve controller isconfigured to receive energy pricing data defining the demand charge andto use the energy pricing data as inputs to the predictive costfunction.
 28. The valve unit of claim 24, wherein the predictive valvecontroller comprises: an economic controller configured to determineoptimal power setpoints for the powered valve components and for thebattery unit at each time step of the optimization period; a trackingcontroller configured to use the optimal power setpoints to determineoptimal position setpoints at each time step of the optimization period;and an equipment controller configured to use the optimal temperaturesetpoints to generate control signals for the powered valve componentsand for the battery unit at each time step of the optimization period.